This document introduces distributional semantic similarity methods for automatically measuring the coherence of topics generated by topic models. It constructs semantic spaces to represent topic words using Wikipedia as a reference corpus. Relatedness between topic words and context features is measured using variants of Pointwise Mutual Information. Topic coherence is determined by measuring the distance between word vectors. Evaluation on three datasets shows distributional measures outperform the state-of-the-art approach, with performance improving using a reduced semantic space.
Roadmap from ESEPaths to EDMPaths: a note on representing annotations resulti...pathsproject
Roadmap from ESEPaths to EDMPaths: a note on representing annotations resulting from automatic enrichment - Aitor Soroa, Eneko Agirre, Arantxa Otegi and Antoine Isaac
This document is a case study on using the Europeana Data Model (EDM) [Doerr et al., 2010] for representing annotations of Cultural Heritage Objects (CHO). One of the main goals of
the PATHS project is to augment CHOs (items) with information that will enrich the user’s experience. The additional information includes links between items in cultural collections and from items to external sources like Wikipedia. With this goal, the PATHS project has applied Natural Language Processing (NLP) techniques on a subset of the items in Europeana.
Implementing Recommendations in the PATHS system, SUEDL 2013pathsproject
Implementing Recommendations in the PATHS system, Paul Clough, Arantxa Otegi, Eneko Agirre and Mark Hall, paper presented at the Supporting Users Exploration of Digital Libraries, SUEDL 2013 workshop, during TPDL 2013 in Valetta, Malta
In this paper we describe the design and implementation of nonpersonalized recommendations in the PATHS system. This system allows users to explore items from Europeana in new ways. Recommendations of the type “people who viewed this item also viewed this item” are powered by pairs of viewed items mined from Europeana. However, due to limited usage data only 10.3% of items in the PATHS dataset have recommendations (4.3% of item pairs visited more than once). Therefore, “related items”, a form of contentbased recommendation, are offered to users based on identifying similar items. We discuss some of the problems with implementing recommendations and highlight areas for future work in the PATHS project.
PATHSenrich: A Web Service Prototype for Automatic Cultural Heritage Item Enr...pathsproject
PATHSenrich: A Web Service Prototype for Automatic Cultural Heritage Item Enrichment, Eneko Agirre, Ander Barrena, Kike Fernandez, Esther Miranda, Arantxa Otegi, and Aitor Soroa, paper presented the international conference on Theory and Practice in Digital Libraries, TPDL 2013
Large amounts of cultural heritage material are nowadays available through online digital library portals. Most of these cultural items have short descriptions and lack rich contextual information. The PATHS project has developed experimental enrichment services. As a proof of concept, this paper presents a web service prototype which allows independent content providers to enrich cultural heritage items with a subset of the full functionality: links to related items in the collection and links to related Wikipedia articles. In the future we plan to provide more advanced functionality, as available offline for PATHS.
User-Centred Design to Support Exploration and Path Creation in Cultural Her...pathsproject
This document describes research on developing a prototype system to enhance user interaction with cultural heritage collections through a pathway metaphor. It involved gathering user requirements through surveys and interviews. Key findings include:
1) Existing online paths tend to be linear and static, limiting exploration, though users preferred more flexible, theme-based paths that allowed branching.
2) Interviews found the path metaphor could represent search histories, journeys of discovery, linked metadata, guides into collections, routes through collections, and more.
3) An interaction model was developed involving consuming, collecting, creating and communicating about paths to support exploration, learning and engagement.
4) The prototype aims to integrate path creation, use and sharing to better support
Open Annotation Collaboration BriefingTimothy Cole
The document summarizes a meeting of the Open Annotation Collaboration (OAC) project team. The OAC aims to develop an interoperable annotation model and specification to facilitate sharing annotations across systems. In phase 1, the OAC will analyze existing annotation practices, develop a data model and specification, integrate annotation tools into Zotero, and create a proof-of-concept implementation.
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
Annotating Digital Texts in the Brown University LibraryTimothy Cole
The document discusses annotating digital texts at Brown University Library. It describes several projects at Brown that involve textual scholarship and digital humanities. It then explains the Pico Project, which aims to annotate Giovanni Pico della Mirandola's 900 Theses. It outlines how annotations of digital objects are ingested and stored in the Brown Digital Repository using AtomPub, XML, RDF, and Linked Data standards to allow for aggregation, syndication, and addressing of annotations.
Roadmap from ESEPaths to EDMPaths: a note on representing annotations resulti...pathsproject
Roadmap from ESEPaths to EDMPaths: a note on representing annotations resulting from automatic enrichment - Aitor Soroa, Eneko Agirre, Arantxa Otegi and Antoine Isaac
This document is a case study on using the Europeana Data Model (EDM) [Doerr et al., 2010] for representing annotations of Cultural Heritage Objects (CHO). One of the main goals of
the PATHS project is to augment CHOs (items) with information that will enrich the user’s experience. The additional information includes links between items in cultural collections and from items to external sources like Wikipedia. With this goal, the PATHS project has applied Natural Language Processing (NLP) techniques on a subset of the items in Europeana.
Implementing Recommendations in the PATHS system, SUEDL 2013pathsproject
Implementing Recommendations in the PATHS system, Paul Clough, Arantxa Otegi, Eneko Agirre and Mark Hall, paper presented at the Supporting Users Exploration of Digital Libraries, SUEDL 2013 workshop, during TPDL 2013 in Valetta, Malta
In this paper we describe the design and implementation of nonpersonalized recommendations in the PATHS system. This system allows users to explore items from Europeana in new ways. Recommendations of the type “people who viewed this item also viewed this item” are powered by pairs of viewed items mined from Europeana. However, due to limited usage data only 10.3% of items in the PATHS dataset have recommendations (4.3% of item pairs visited more than once). Therefore, “related items”, a form of contentbased recommendation, are offered to users based on identifying similar items. We discuss some of the problems with implementing recommendations and highlight areas for future work in the PATHS project.
PATHSenrich: A Web Service Prototype for Automatic Cultural Heritage Item Enr...pathsproject
PATHSenrich: A Web Service Prototype for Automatic Cultural Heritage Item Enrichment, Eneko Agirre, Ander Barrena, Kike Fernandez, Esther Miranda, Arantxa Otegi, and Aitor Soroa, paper presented the international conference on Theory and Practice in Digital Libraries, TPDL 2013
Large amounts of cultural heritage material are nowadays available through online digital library portals. Most of these cultural items have short descriptions and lack rich contextual information. The PATHS project has developed experimental enrichment services. As a proof of concept, this paper presents a web service prototype which allows independent content providers to enrich cultural heritage items with a subset of the full functionality: links to related items in the collection and links to related Wikipedia articles. In the future we plan to provide more advanced functionality, as available offline for PATHS.
User-Centred Design to Support Exploration and Path Creation in Cultural Her...pathsproject
This document describes research on developing a prototype system to enhance user interaction with cultural heritage collections through a pathway metaphor. It involved gathering user requirements through surveys and interviews. Key findings include:
1) Existing online paths tend to be linear and static, limiting exploration, though users preferred more flexible, theme-based paths that allowed branching.
2) Interviews found the path metaphor could represent search histories, journeys of discovery, linked metadata, guides into collections, routes through collections, and more.
3) An interaction model was developed involving consuming, collecting, creating and communicating about paths to support exploration, learning and engagement.
4) The prototype aims to integrate path creation, use and sharing to better support
Open Annotation Collaboration BriefingTimothy Cole
The document summarizes a meeting of the Open Annotation Collaboration (OAC) project team. The OAC aims to develop an interoperable annotation model and specification to facilitate sharing annotations across systems. In phase 1, the OAC will analyze existing annotation practices, develop a data model and specification, integrate annotation tools into Zotero, and create a proof-of-concept implementation.
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
Annotating Digital Texts in the Brown University LibraryTimothy Cole
The document discusses annotating digital texts at Brown University Library. It describes several projects at Brown that involve textual scholarship and digital humanities. It then explains the Pico Project, which aims to annotate Giovanni Pico della Mirandola's 900 Theses. It outlines how annotations of digital objects are ingested and stored in the Brown Digital Repository using AtomPub, XML, RDF, and Linked Data standards to allow for aggregation, syndication, and addressing of annotations.
This document discusses annotation services provided by Brown University Library for annotating digital texts. It describes several digital humanities projects at Brown that involve annotation. It then explains how the library uses AtomPub and RDF to publish annotations on the web as Linked Open Data with metadata and links back to the annotated sources. Users can annotate portions of documents and their annotations will be ingested into the repository and syndicated as Atom feeds that others can subscribe to.
This chapter introduces the concept of data and discusses some examples to illustrate key ideas:
1) The same data can be summarized in different ways (e.g. mean vs median) leading to different conclusions.
2) Self-selected samples from online polls cannot be used to make statistical inferences about a population.
3) Randomized experiments are needed to establish cause-and-effect relationships and eliminate bias, but industry-funded research may still be biased in its methods or presentation.
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCESijnlc
Relation extraction is one of the most important parts of natural language processing. It is the process of extracting relationships from a text. Extracted relationships actually occur between two or more entities of a certain type and these relations may have different patterns. The goal of the paper is to find out the noisy patterns for relation extraction of Bangla sentences. For the research work, seed tuples were needed containing two entities and the relation between them. We can get seed tuples from Freebase. Freebase is a large collaborative knowledge base and database of general, structured information for public use. But for Bangla language, there is no available Freebase. So we made Bangla Freebase which was the real challenge and it can be used for any other NLP based works. Then we tried to find out the noisy patterns for relation extraction by measuring conflict score.
The document discusses the role of text mining in search engines. It describes how search engines work by crawling websites and indexing key terms. Text mining can help search engines provide more relevant and contextualized search results through techniques like clustering, categorization, and entity extraction. The document also discusses future trends in search engines leveraging more advanced text mining techniques like summarization and answering intelligent questions.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Information extraction involves extracting structured information from unstructured text. The goal is to identify named entities, relations between entities, and populate a database. This may also include event extraction, resolving temporal expressions, and wrapper induction. Common tasks include named entity recognition, co-reference resolution, relation extraction, and event extraction. Statistical methods like conditional random fields are often used. Evaluation involves measuring precision and recall.
The document discusses searching for answers to keyword queries in linked data. It presents the problem of keyword query routing, which aims to identify a valid set of data sources that can produce non-empty answers to a keyword query. It proposes using keyword-element relationship graphs at the element, schema, and data source levels to model relationships between keywords and data elements or sources. Experiments on a chunk of the Billion Triple Challenge dataset indicate that considering relationships between elements within a maximum path length performs better than considering only direct relationships, and identifies valid plans for multi-source queries.
Visualization approaches in text mining emphasize making large amounts of data easily accessible and identifying patterns within the data. Common visualization tools include simple concept graphs, histograms, line graphs, and circle graphs. These tools allow users to quickly explore relationships within text data and gain insights that may not be apparent from raw text alone. Architecturally, visualization tools are layered on top of text mining systems' core algorithms and allow for modular integration of different visualization front ends.
This document discusses modelling and representing social network data ontologically. It covers representing social individuals and relationships ontologically, as well as aggregating and reasoning with social network data. It discusses ontology languages like RDF, OWL, and FOAF that can be used to represent social network data and individuals semantically. It also talks about state-of-the-art approaches for representing network structure and attribute data, and the need for representations that can integrate different data sources and maintain identity.
An Efficient Annotation of Search Results Based on Feature Ranking Approach f...Computer Science Journals
With the increased number of web databases, major part of deep web is one of the bases of database. In several search engines, encoded data in the returned resultant pages from the web often comes from structured databases which are referred as Web databases (WDB).
(1) The document discusses the Semantic Web, ontologies, and ontology learning. It defines the Semantic Web as an extension of the current web that gives information well-defined meaning. (2) Ontologies are formal specifications of concepts and relations that provide shared meanings between machines and humans. (3) Ontology learning is the automatic or semi-automatic process of extracting ontological concepts and relations from text to build or enrich ontologies. The document outlines methods for ontology learning and its applications.
Text mining seeks to extract useful information from unstructured text documents. It involves preprocessing the text, identifying features, and applying techniques from data mining, machine learning and natural language processing to discover patterns. The core operations of text mining include analyzing distributions of concepts, identifying frequent concept sets and associations between concepts. Text mining systems aim to analyze document collections over time to identify trends, ephemeral relationships and anomalous patterns.
The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.
The document discusses the Phonotonetic Chinese Language System, which aims to provide a 1-to-1 correspondence between the spoken sounds and written symbols of Mandarin Chinese. It also discusses how this system provides ordering of both words and logographs, enabling software in other languages to work with Mandarin. Finally, it explains how this system helps address issues around collation and searching of written Chinese.
The Semantic Web is a vision of information that is understandable by computers. Although there is great exploitable potential, we are still in "Generation Zero'' of the Semantic Web, since there are few real-world compelling applications. The heterogeneity, the volume of data and the lack of standards are problems that could be addressed through some nature inspired methods. The paper presents the most important aspects of the Semantic Web, as well as its biggest issues; it then describes some methods inspired from nature - genetic algorithms, artificial neural networks, swarm intelligence, and the way these techniques can be used to deal with Semantic Web problems.
This document provides an overview of information retrieval models, including vector space models, TF-IDF, Doc2Vec, and latent semantic analysis. It begins with basic concepts in information retrieval like document indexing and relevance scoring. Then it discusses vector space models and how documents and queries are represented as vectors. TF-IDF weighting is explained as assigning higher weight to rare terms. Doc2Vec is introduced as an extension of word2vec to learn document embeddings. Latent semantic analysis uses singular value decomposition to project documents to a latent semantic space. Implementation details and examples are provided for several models.
1) The document discusses various services for open access literature including institutional archives, metadata harvesting through Celestial, and citation analysis services like Citebase.
2) It describes how Citebase extracts references from texts and stores them in a structured database to enable citation linking and navigation between cited and citing articles.
3) Early download frequency data from arXiv.org is shown to correlate with longer-term citation frequency, indicating web impact may predict future citation impact.
Hypertext links text to other documents or locations within documents in a non-linear way. Users can browse hypertext by following links and using the back button to navigate. Automatic indexes of the web are created by crawling pages and extracting keywords. URLs uniquely identify internet resources and can point to specific locations within pages using fragment identifiers. HTML links are created using the <a> tag, which can link to other pages or locations within the current page.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Exchange In-Place eDiscovery & Hold | Introduction | 5#7Eyal Doron
Exchange In-Place eDiscovery & Hold | Introduction | 5#7
http://o365info.com/exchange-in-place-ediscovery-hold-introduction-part-5-7
The Exchange In-Place Hold & eDiscovery, is a very powerful tool that can help us to accomplish three main tasks.
1. Search for information (mail items) in single or multiple mailboxes
2. Put specific information on “hold” (enable to save the information for an unlimited time period)
3. Recover deleted mail items
In this article, we will review the logic and the concepts of the Exchange In-Place Hold & eDiscovery toll.
In the next article xx, we will demonstrate how to use the Exchange In-Place Hold & eDiscovery toll for recovering deleted mail items.
Eyal Doron | o365info.com
This document discusses annotation services provided by Brown University Library for annotating digital texts. It describes several digital humanities projects at Brown that involve annotation. It then explains how the library uses AtomPub and RDF to publish annotations on the web as Linked Open Data with metadata and links back to the annotated sources. Users can annotate portions of documents and their annotations will be ingested into the repository and syndicated as Atom feeds that others can subscribe to.
This chapter introduces the concept of data and discusses some examples to illustrate key ideas:
1) The same data can be summarized in different ways (e.g. mean vs median) leading to different conclusions.
2) Self-selected samples from online polls cannot be used to make statistical inferences about a population.
3) Randomized experiments are needed to establish cause-and-effect relationships and eliminate bias, but industry-funded research may still be biased in its methods or presentation.
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCESijnlc
Relation extraction is one of the most important parts of natural language processing. It is the process of extracting relationships from a text. Extracted relationships actually occur between two or more entities of a certain type and these relations may have different patterns. The goal of the paper is to find out the noisy patterns for relation extraction of Bangla sentences. For the research work, seed tuples were needed containing two entities and the relation between them. We can get seed tuples from Freebase. Freebase is a large collaborative knowledge base and database of general, structured information for public use. But for Bangla language, there is no available Freebase. So we made Bangla Freebase which was the real challenge and it can be used for any other NLP based works. Then we tried to find out the noisy patterns for relation extraction by measuring conflict score.
The document discusses the role of text mining in search engines. It describes how search engines work by crawling websites and indexing key terms. Text mining can help search engines provide more relevant and contextualized search results through techniques like clustering, categorization, and entity extraction. The document also discusses future trends in search engines leveraging more advanced text mining techniques like summarization and answering intelligent questions.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Information extraction involves extracting structured information from unstructured text. The goal is to identify named entities, relations between entities, and populate a database. This may also include event extraction, resolving temporal expressions, and wrapper induction. Common tasks include named entity recognition, co-reference resolution, relation extraction, and event extraction. Statistical methods like conditional random fields are often used. Evaluation involves measuring precision and recall.
The document discusses searching for answers to keyword queries in linked data. It presents the problem of keyword query routing, which aims to identify a valid set of data sources that can produce non-empty answers to a keyword query. It proposes using keyword-element relationship graphs at the element, schema, and data source levels to model relationships between keywords and data elements or sources. Experiments on a chunk of the Billion Triple Challenge dataset indicate that considering relationships between elements within a maximum path length performs better than considering only direct relationships, and identifies valid plans for multi-source queries.
Visualization approaches in text mining emphasize making large amounts of data easily accessible and identifying patterns within the data. Common visualization tools include simple concept graphs, histograms, line graphs, and circle graphs. These tools allow users to quickly explore relationships within text data and gain insights that may not be apparent from raw text alone. Architecturally, visualization tools are layered on top of text mining systems' core algorithms and allow for modular integration of different visualization front ends.
This document discusses modelling and representing social network data ontologically. It covers representing social individuals and relationships ontologically, as well as aggregating and reasoning with social network data. It discusses ontology languages like RDF, OWL, and FOAF that can be used to represent social network data and individuals semantically. It also talks about state-of-the-art approaches for representing network structure and attribute data, and the need for representations that can integrate different data sources and maintain identity.
An Efficient Annotation of Search Results Based on Feature Ranking Approach f...Computer Science Journals
With the increased number of web databases, major part of deep web is one of the bases of database. In several search engines, encoded data in the returned resultant pages from the web often comes from structured databases which are referred as Web databases (WDB).
(1) The document discusses the Semantic Web, ontologies, and ontology learning. It defines the Semantic Web as an extension of the current web that gives information well-defined meaning. (2) Ontologies are formal specifications of concepts and relations that provide shared meanings between machines and humans. (3) Ontology learning is the automatic or semi-automatic process of extracting ontological concepts and relations from text to build or enrich ontologies. The document outlines methods for ontology learning and its applications.
Text mining seeks to extract useful information from unstructured text documents. It involves preprocessing the text, identifying features, and applying techniques from data mining, machine learning and natural language processing to discover patterns. The core operations of text mining include analyzing distributions of concepts, identifying frequent concept sets and associations between concepts. Text mining systems aim to analyze document collections over time to identify trends, ephemeral relationships and anomalous patterns.
The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.
The document discusses the Phonotonetic Chinese Language System, which aims to provide a 1-to-1 correspondence between the spoken sounds and written symbols of Mandarin Chinese. It also discusses how this system provides ordering of both words and logographs, enabling software in other languages to work with Mandarin. Finally, it explains how this system helps address issues around collation and searching of written Chinese.
The Semantic Web is a vision of information that is understandable by computers. Although there is great exploitable potential, we are still in "Generation Zero'' of the Semantic Web, since there are few real-world compelling applications. The heterogeneity, the volume of data and the lack of standards are problems that could be addressed through some nature inspired methods. The paper presents the most important aspects of the Semantic Web, as well as its biggest issues; it then describes some methods inspired from nature - genetic algorithms, artificial neural networks, swarm intelligence, and the way these techniques can be used to deal with Semantic Web problems.
This document provides an overview of information retrieval models, including vector space models, TF-IDF, Doc2Vec, and latent semantic analysis. It begins with basic concepts in information retrieval like document indexing and relevance scoring. Then it discusses vector space models and how documents and queries are represented as vectors. TF-IDF weighting is explained as assigning higher weight to rare terms. Doc2Vec is introduced as an extension of word2vec to learn document embeddings. Latent semantic analysis uses singular value decomposition to project documents to a latent semantic space. Implementation details and examples are provided for several models.
1) The document discusses various services for open access literature including institutional archives, metadata harvesting through Celestial, and citation analysis services like Citebase.
2) It describes how Citebase extracts references from texts and stores them in a structured database to enable citation linking and navigation between cited and citing articles.
3) Early download frequency data from arXiv.org is shown to correlate with longer-term citation frequency, indicating web impact may predict future citation impact.
Hypertext links text to other documents or locations within documents in a non-linear way. Users can browse hypertext by following links and using the back button to navigate. Automatic indexes of the web are created by crawling pages and extracting keywords. URLs uniquely identify internet resources and can point to specific locations within pages using fragment identifiers. HTML links are created using the <a> tag, which can link to other pages or locations within the current page.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Exchange In-Place eDiscovery & Hold | Introduction | 5#7Eyal Doron
Exchange In-Place eDiscovery & Hold | Introduction | 5#7
http://o365info.com/exchange-in-place-ediscovery-hold-introduction-part-5-7
The Exchange In-Place Hold & eDiscovery, is a very powerful tool that can help us to accomplish three main tasks.
1. Search for information (mail items) in single or multiple mailboxes
2. Put specific information on “hold” (enable to save the information for an unlimited time period)
3. Recover deleted mail items
In this article, we will review the logic and the concepts of the Exchange In-Place Hold & eDiscovery toll.
In the next article xx, we will demonstrate how to use the Exchange In-Place Hold & eDiscovery toll for recovering deleted mail items.
Eyal Doron | o365info.com
A pilot on Semantic Textual Similaritypathsproject
This document summarizes the SemEval 2012 task on semantic textual similarity. It describes the motivation for the task as measuring similarity between text fragments on a graded scale. It then outlines the datasets used, including the MSR paraphrase corpus, MSR video corpus, WMT evaluation data, and OntoNotes word sense data. It also discusses the annotation process, which involved a pilot with authors and crowdsourcing through Mechanical Turk. The results showed most systems performed better than baselines and the best systems achieved correlations over 0.8 with human judgments.
SemEval-2012 Task 6: A Pilot on Semantic Textual Similaritypathsproject
This document describes the SemEval-2012 Task 6 on semantic textual similarity. The task involved measuring the semantic equivalence of sentence pairs on a scale from 0 to 5. The training data consisted of 2000 sentence pairs from existing paraphrase and machine translation datasets. The test data also had 2000 sentence pairs from these datasets as well as surprise datasets. Systems were evaluated based on their Pearson correlation with human annotations. 35 teams participated and the best systems achieved a Pearson correlation over 80%. This pilot task established semantic textual similarity as an area for further exploration.
Microsoft connectivity analyzer (mca) autodiscover troubleshooting tools pa...Eyal Doron
The document discusses the Microsoft Connectivity Analyzer (MCA) tool, which is used to test the Autodiscover process from within an organization's network or from a specific user's desktop. It provides instructions on downloading, installing, and using the MCA tool to test Autodiscover and Outlook Anywhere configurations. The MCA combines Autodiscover and Outlook Anywhere tests into a single wizard-based interface, while the Remote Connectivity Analyzer (ExRCA) allows testing them separately. The MCA has limitations compared to ExRCA, such as an inability to test Active Directory-based Autodiscover configurations.
Autodiscover flow in an exchange on premises environment non-active director...Eyal Doron
Autodiscover flow in an Exchange on-Premises environment | non-Active Directory environment| Part 2#3 | Part 27#36
Detailed description of the Autodiscover flow that is implemented between Autodiscover client and his Autodiscover Endpoint (Exchange server) in a scenario, in which the Exchange infrastructure is - Exchange on-Premises and, the Autodiscover Endpoint is located in a non-Active Directory based environment.
This is the second article, in a series of three articles.
http://o365info.com/autodiscover-flow-in-an-exchange-on-premises-environment-non-active-directory-environment-part-2-of-3-part-27-of-36
Eyal Doron | o365info.com
My E-mail appears as spam | The 7 major reasons | Part 5#17Eyal Doron
My E-mail appears as spam | The 7 major reasons | Part 5#17
http://o365info.com/my-e-mail-appears-as-spam-the-7-major-reasons-part-5-17
Review three major reasons, that could lead to a scenario, in which E-mail that is sent from our organization identified as spam mail:
1. E-mail content, 2. Violation of the SMTP standards, 3. Bulk\Mass mail
Eyal Doron | o365info.com
Ашманов И.С. (2013.04.24) - Информационный суверенитет - новая реальностьmediamera
За последние 10, 15, 20 лет слом суверенитета часто происходит через информационные войны. Если вам удается добиться информационного доминирования в какой-то стране — это все-равно, что господствовать в воздухе. В Ливии все именно так и начиналось: сначала произошел слом информационного суверенитета, вызов цветной революции, а потом небо сделали бесполетной зоной и сломили сопротивление. Таким образом, сегодня, если у вас нет информационного суверенитета, вы можете потерять обычный суверенитет, то есть перестать быть независимым государством.
Что такое цифровой суверенитет? Это право государства определять свою информационную политику самостоятельно, распоряжаться инфраструктурой, ресурсами, обеспечивать информационную безопасность и т.д.
Для того, чтобы построить информационный суверенитет, необходимо иметь единую инфраструктуру: собственные поисковые системы, собственные социальные сети, свои месседжеры, блоги, контентные ресурсы и т.д. Также нужно иметь средства мониторинга персональной среды, средства фильтрации трафика. Для защиты от кибервойн необходимо иметь кибервойска.
(1) The document describes a village cleaning initiative undertaken by students at Satya Elemantary School in Dasaniya, Rajasthan, India.
(2) The students felt that illiteracy and a dirty village were major issues, so they organized a cleaning effort to remove weeds from public places like the hospital, bus station, and post office.
(3) Through their cleaning initiative, the students aimed to improve sanitation and reduce health issues in their community caused by the dirty areas. Their efforts had a positive impact on both the community and the students themselves.
The old exchange environment versus modern exchange environment part 02#36Eyal Doron
The old Exchange environment versus “modern” Exchange environment | Part 02#36
The Autodiscover as a solution for the modern Exchange environment versus, the older Exchange server architecture that did not have the Autodiscover infrastructure.
http://o365info.com/the-old-exchange-environment-versus-modern-exchange-environment-part-02-of-36
Eyal Doron | o365info.com
Evaluating the Use of Clustering for Automatically Organising Digital Library...pathsproject
Presentation given by Mark M. Hall, Mark Stevenson and Paul D. Clough from the Information School /Department of Computer Science, University of Sheffield, UK
24-27 September 2012
TPDL 2012, Cyprus
Topic Modeling for Information Retrieval and Word Sense Disambiguation tasksLeonardo Di Donato
Experimental work done regarding the use of Topic Modeling for the implementation and the improvement of some common tasks of Information Retrieval and Word Sense Disambiguation.
First of all it describes the scenario, the pre-processing pipeline realized and the framework used. After we we face a discussion related to the investigation of some different hyperparameters configurations for the LDA algorithm.
This work continues dealing with the retrieval of relevant documents mainly through two different approaches: inferring the topics distribution of the held out document (or query) and comparing it to retrieve similar collection’s documents or through an approach driven by probabilistic querying. The last part of this work is devoted to the investigation of the word sense disambiguation task.
This document summarizes and compares two computerized methods for analyzing political text documents: Wordscores and Wordfish. Wordscores relies on selecting reference texts to anchor its analysis, while Wordfish treats each text as its own data point without reference texts. Studies have found Wordscores performs better when positions are less polarized or data is limited, while Wordfish performs equally well or better when positions are more polarized. Both methods have limitations, such as Wordscores' inability to reliably analyze changes in word meaning over time and Wordfish's treatment of all words equally. Overall, the document evaluates the strengths and weaknesses of these two automated content analysis techniques.
Concurrent Inference of Topic Models and Distributed Vector RepresentationsParang Saraf
Abstract: Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated in the probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces distributed representation of topics to capture topical themes in a dataset. Unlike many state-of-the-art techniques for generating distributed representation of words and documents that directly use neighboring words for training, we leverage the outcome of a sophisticated deep neural network to estimate the topic labels of each document. The networks, for topic modeling and generation of distributed representations, are trained concurrently in a cascaded style with better runtime without sacrificing the quality of the topics. Empirical studies reported in the paper show that the distributed representations of topics represent intuitive themes using smaller dimensions than conventional topic modeling approaches.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Cooperating Techniques for Extracting Conceptual Taxonomies from TextFulvio Rotella
The document proposes a mixed approach using existing natural language processing techniques and novel techniques to automatically construct conceptual taxonomies from text. It identifies relevant concepts from text using keyword extraction, clustering, and computing relevance weights. It then generalizes similar concepts using WordNet to group concepts and disambiguate word senses. Preliminary evaluations show promising initial results.
The document proposes a mixed approach using existing natural language processing techniques and novel techniques to automatically construct conceptual taxonomies from text. Key steps include identifying relevant concepts and attributes from text, clustering similar concepts, computing relevance weights for concepts, and generalizing concepts using WordNet. Preliminary results suggest the approach shows promise for extending and improving automatic taxonomy construction.
Automatize Document Topic And Subtopic Detection With Support Of A CorpusRichard Hogue
This document discusses previous research on automatic topic and subtopic detection from documents. It provides an overview of various approaches that have been used, including text segmentation, text clustering, frequent word sequences, word clustering, concept-based classification, probabilistic topic modeling, and agglomerative clustering. The document then proposes a new method called paragraph extension, which treats a document as a set of paragraphs and uses a paragraph merging technique and corpus of related words to detect topics and subtopics.
The document discusses two neural network models for reading comprehension tasks: the Attentive Reader model proposed by Herman et al. in 2015 and the Stanford Reader model proposed by Chen et al. in 2016. The author implemented a two-layer attention model inspired by these previous models that achieves a 1.5% higher accuracy on reading comprehension tasks compared to the Stanford Reader.
In this paper we tried to correlate text sequences those provides common topics for semantic clues. We propose a two step method for asynchronous text mining. Step one check for the common topics in the sequences and isolates these with their timestamps. Step two takes the topic and tries to give the timestamp of the text document. After multiple repetitions of step two, we could give optimum result.
The document presents a knowledge-based method for measuring semantic similarity between texts. It combines word-to-word semantic similarity metrics with information about word specificity to calculate a text-to-text similarity score. An example application shows how word similarity scores from WordNet are combined using the Wu & Palmer metric to determine the semantic similarity between two text segments. The method is evaluated on paraphrase identification tasks and shown to outperform approaches based only on lexical matching.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
This document provides an introduction and overview of 5 papers related to topic modeling techniques. It begins with introducing the speaker and their research interests in text analysis using topic modeling. It then lists the 5 papers that will be discussed: LSA, pLSI, LDA, Gaussian LDA, and criticisms of topic modeling. The document focuses on summarizing each paper's motivation, key points, model, parameter estimation methods, and deficiencies. It provides high-level summaries of key aspects of influential topic modeling papers to introduce the topic.
Ontology Matching Based on hypernym, hyponym, holonym, and meronym Sets in Wo...dannyijwest
Considerable research in the field of ontology matching has been performed where information sharing
and reuse becomes necessary in ontology development. Measurement of lexical similarity in ontology
matching is performed using synset, defined in WordNet. In this paper, we defined a Super Word Set,
which is an aggregate set that includes hypernym, hyponym, holonym, and meronym sets in WordNet.
The Super Word Set Similarity is calculated by the rate of words of concept name and synset’s words
inclusion in the Super Word Set. In order to measure of Super Word Set Similarity, we first extracted
Matched Concepts(MC), Matched Properties(MP) and Property Unmatched Concepts(PUC) from the
result of ontology matching. We compared these against two ontology matching tools – COMA++ and
LOM. The Super Word Set Similarity shows an average improvement of 12% over COMA++ and 19%
over LOM.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESkevig
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESkevig
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
Co-word analyses study the co-occurrence of pairs of items (for example, keywords) that are representative in a document, to identify relations between the ideas presented in the
texts.
The document summarizes techniques for identifying themes in qualitative research. It discusses that themes are abstract constructs that link expressions and can come in various shapes. Themes can come from the data, investigator's prior understanding, characteristics of the phenomenon, definitions, common constructs, and personal experiences. It outlines several techniques for identifying themes, including repetitions, indigenous typologies/categories, metaphors and analogies, transitions, similarities and differences, linguistic connectors, missing data, theory-related material, and processing techniques like cutting and sorting, multi-dimensional scaling, words and keywords in context, and word co-occurrence. The document evaluates different techniques based on the type of data, required expertise, labor required, and number/types of themes to
The document describes latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA represents documents as random mixtures over latent topics, characterized by distributions over words. It is a three-level hierarchical Bayesian model where documents are generated by first sampling a per-document topic distribution from a Dirichlet prior, then repeatedly sampling topics and words from these distributions. LDA addresses limitations of previous models by capturing statistical structure within and between documents through the hierarchical Bayesian formulation.
In recent years, great advances have been made in the speed, accuracy, and coverage of automatic word
sense disambiguator systems that, given a word appearing in a certain context, can identify the sense of
that word. In this paper we consider the problem of deciding whether same words contained in different
documents are related to the same meaning or are homonyms. Our goal is to improve the estimate of the
similarity of documents in which some words may be used with different meanings. We present three new
strategies for solving this problem, which are used to filter out homonyms from the similarity computation.
Two of them are intrinsically non-semantic, whereas the other one has a semantic flavor and can also be
applied to word sense disambiguation. The three strategies have been embedded in an article document
recommendation system that one of the most important Italian ad-serving companies offers to its customers.
In recent years, great advances have been made in the speed, accuracy, and coverage of automatic word
sense disambiguator systems that, given a word appearing in a certain context, can identify the sense of
that word. In this paper we consider the problem of deciding whether same words contained in different
documents are related to the same meaning or are homonyms. Our goal is to improve the estimate of the
similarity of documents in which some words may be used with different meanings. We present three new
strategies for solving this problem, which are used to filter out homonyms from the similarity computation.
Two of them are intrinsically non-semantic, whereas the other one has a semantic flavor and can also be
applied to word sense disambiguation. The three strategies have been embedded in an article document
recommendation system that one of the most important Italian ad-serving companies offers to its customers
NOVELTY DETECTION VIA TOPIC MODELING IN RESEARCH ARTICLES cscpconf
In today’s world redundancy is the most vital problem faced in almost all domains. Novelty detection is the identification of new or unknown data or signal that a machine learning system
is not aware of during training. The problem becomes more intense when it comes to “Research Articles”. A method of identifying novelty at each sections of the article is highly required for determining the novel idea proposed in the research paper. Since research articles are semistructured,detecting novelty of information from them requires more accurate systems. Topic model provides a useful means to process them and provides a simple way to analyze them. This work compares the most predominantly used topic model- Latent Dirichlet Allocation with the hierarchical Pachinko Allocation Model. The results obtained are promising towards hierarchical Pachinko Allocation Model when used for document retrieval.
Similar to Aletras, Nikolaos and Stevenson, Mark (2013) "Evaluating Topic Coherence Using Distributional Semantics” (IWCS 2013) (20)
Generating Paths through Cultural Heritage Collections Latech2013 paperpathsproject
Generating Paths through Cultural Heritage Collections, Samuel Fernando, Paula Goodale, Paul Clough, Mark Stevenson, Mark Hall and Eneko Agirre. Paper presented at Latech 2013
Cultural heritage collections usually organise sets of items into exhibitions or guided tours. These items are often accompanied by text that describes the theme and topic of the exhibition and provides background context and details of connections with other items. The PATHS project brings the idea of guided tours to digital library collections where a tool to create virtual paths are used to assist with navigation and provide guides on particular subjects and topics. In this paper we characterise and analyse paths of items created by users of our online system.
Supporting User's Exploration of Digital Libraries, Suedl 2012 workshop proce...pathsproject
Workshop proceedings from the International workshop on Supporting Users Exploration of Digital Libraries, SUEDL 2012 which was held at TPDL 2012 (the international conference on Theory and Practice in Digital Libraries), Paphos, Cyprus, September 2012.
The aim of the workshop was to stimulate collaboration from experts and stakeholders in Digital Libraries, Cultural Heritage, Natural Language Processing and Information Retrieval in order to explore methods and strategies to support exploration of Digital Libraries, beyond the white box paradigm of search and click.
The proceedings includes:
"Browsing Europeana - Opportunities and Challenges', David Haskiya
"Query re-writing using shallow language processing effects', Anna Mastora and Sarantos Kapidakis
"Visualising Television Heritage" Johan Ooman et al,
"Providing suitable information access for new users of Digital Libraries", Rike Brecht et al
"Exploring Pelagios: a Visual Browser for Geo-tagged datasets" Rainer Simon et al
PATHS state of the art monitoring reportpathsproject
This document provides an update to an Initial State of the Art Monitoring report delivered by the project. The report covers the areas of Educational Informatics, Information Retrieval and Semantic Similarity relatedness.
Recommendations for the automatic enrichment of digital library content using...pathsproject
Recommendations for the enrichment of digital library content using open source software, PATHS report by Eneko Agirre and Arantxa Otegi
The goal of this document is to present an overall set of recommendations for the automatic enrichment of Digital Library content using open source software. It is intended to be useful for third-parties who would like to offer enrichment services. Note that this is not a step-by-step guide for reimplementation, but an overall view of the software required and the programming effort involved.
Semantic Enrichment of Cultural Heritage content in PATHSpathsproject
Semantic Enrichment of Cultural Heritage content in PATHS, report by Mark Stevenson and Arantxa Otegi with Eneko Agirre, Nikos Aletras, Paul Clough, Samuel Fernando and Aitor Saroa.
The aim of the PATHS project is to enable exploration and discovery within cultural heritage collections. In order to support this the project developed a range of enrichment techniques which augmented these collections with additional information to enhance the users’ browsing experience. One of the demonstration systems developed in PATHS makes use of content from Europeana. This document summarises the semantic enrichment techniques developed in PATHS, with particular reference to their application to the Europeana data.
Generating Paths through Cultural Heritage Collections, LATECH 2013 paperpathsproject
Generating Paths through Cultural Heritage Collections Samuel Fernando, Paula Goodale, Paul Clough, Mark Stevenson, Mark Hall and Eneko Agirre.
The PATHS project brings the idea of guided tours to digital library collections where a tool to create virtual paths are used to assist with navigation and provide guides on particular subjects and topics. In this paper we characterise and analyse paths of items created by users of our online system.
Generating PATHS through Cultural Heritage Collections, Samuel Fernando, Paula Goodale, Paul Clough,
Mark Stevenson, Mark Hall, Eneko Agirre. Presentation given at LaTeCH 2013, ACL Workshop, Sofia, Bulgaria.
The PATHS project is a 3-year EU-funded project involving 6 partners across 5 countries. The project aims to introduce personalized paths into digital cultural heritage collections to provide more engaging access to large volumes of online material. The PATHS system enriches metadata through natural language processing and links items within collections and to external resources. It provides various tools for browsing, searching and creating paths. Two rounds of user evaluations found the path creation tools and search mechanisms were well received. Outcomes include the PATHS API and potential commercialization of components and consultancy services.
This document summarizes the PATHS project, which developed tools for exploring digital cultural heritage collections. The project involved 6 partners across 5 countries. It researched methods for navigating collections, including user-created paths and natural language processing of metadata. Users can browse collections through a thesaurus, tag cloud, or topic map. The system allows users to create and publish nonlinear paths through the collection with descriptions. The tools have potential for classroom activities, curated collections, and research.
Comparing taxonomies for organising collections of documents presentationpathsproject
This document compares different taxonomies for organizing large collections of documents. It evaluates taxonomies that were either manually created (LCSH, WordNet domains, Wikipedia taxonomy, DBpedia ontology) or automatically derived from document data using LDA topic modeling or Wikipedia link frequencies. The document describes applying these taxonomies to a collection of over 550,000 items from Europeana, a digital library. It then evaluates the taxonomies based on how cohesive the groupings are and how accurately the relationships between parent and child nodes are classified.
Comparing taxonomies for organising collections of documentspathsproject
This document compares different taxonomies for organizing large collections of documents. It examines four existing manually created taxonomies (Library of Congress Subject Headings, WordNet Domains, Wikipedia Taxonomy, DBpedia) and two methods for automatically deriving taxonomies (WikiFreq and LDA topics) for organizing a large online cultural heritage collection from Europeana. It then presents two human evaluations of the taxonomies, measuring cohesion and analyzing concept relations, and finds that the manual taxonomies have high-quality relations while the novel automatic method generates very high cohesion.
PATHS Final prototype interface design v1.0pathsproject
This document summarizes the design methodology and current status of the interface design for the second prototype of the PATHS project. It begins with a three-stage design methodology that includes: evaluating the first prototype design process, creating low-fidelity storyboards, and developing high-fidelity interaction designs. It then reviews lessons learned from developing the first prototype interface. The document introduces new user interface components and presents preliminary high-fidelity designs for key pages like the landing page, path editing, and item pages. Expert evaluation of the designs is planned along with user evaluation of a working prototype. The goal is to address issues identified in prior evaluations and create an intuitive interface for the PATHS cultural heritage system.
PATHS Evaluation of the 1st paths prototypepathsproject
This document summarizes the evaluation of the first prototype of the PATHS project. It describes the evaluation methodology, which included field-based demonstrations and laboratory evaluations. Results are presented from both types of evaluations, including participant demographics, user feedback on ease of use and usefulness of PATHS, suggested improvements, and results from structured tasks conducted in the laboratory evaluations. The document also reviews how well the first PATHS prototype met its functional specifications.
PATHS Second prototype-functional-specpathsproject
This document provides the functional specifications for the second prototype of the PATHS project. It outlines functions to be implemented based on priorities - functions that resolve critical user interface issues are highest priority. The second prototype will address issues identified in testing the first prototype and provide enhanced functionality such as improved search, user accounts/permissions, and workspace customization. The specifications draw on recommendations from evaluating the first prototype to improve the PATHS system.
PATHS Final state of art monitoring report v0_4pathsproject
This document provides an update on the state of the art in several areas relevant to the PATHS project, including educational informatics, information retrieval, semantic similarity, and wikification. In educational informatics, the document discusses different approaches to evaluating cognitive styles and selects Riding's Cognitive Style Analysis as most appropriate for PATHS. In information retrieval, a paper outlines long-term research objectives, including moving beyond ranked lists and helping users. The document also discusses recent advances in measuring semantic textual similarity using new datasets, as well as improved accuracy in wikification and detecting related articles. New sections cover crowdsourcing, including its use for cultural heritage tasks, and the mobile web, focusing on responsive design principles.
The document summarizes the first prototype of the PATHS system, which aims to make exploring cultural heritage collections enjoyable and easy for users. It describes the three main components of the prototype: 1) a web application user interface built with Python, 2) a .NET XML web service API that the interface uses to access data, and 3) a PostgreSQL database that stores processed cultural heritage items and other application data according to a logical data model. Screenshots and documentation are provided to demonstrate the prototype's core functionality and potential navigation, retrieval, and content enrichment capabilities. The prototype will be evaluated and used to inform the development of a second, improved version.
Here are the key changes to ESEpaths described in the document:
- ESEpaths has been extended to include informativeness scores, normalized dates from <dc:date> field, vocabulary terms for tag clouds, event information, sentiment information at item level, typed related items, title and sentiment of background links.
- For easier ingestion and production, the data will now be separated into different ESEpaths files, with a single item having several ESE and ESEpaths files holding complementary information.
- A proposal for EDMpaths is presented, which would represent the enrichment done in ESEpaths but using the Europeana Data Model (EDM) format instead of ESE.
- ESE
This report accompanies a deliverable from the PATHS project which processed and enriched content from cultural heritage collections for use in a prototype. It describes the collections included from Europeana and a private collection. The content was analyzed using natural language processing and enriched with terms from controlled vocabularies. Links between items within and across collections were also computed based on text similarity. The deliverable contains the processed data along with documentation of the methodologies used to enrich and link the cultural heritage content.
This document specifies the system architecture for the PATHS project. The PATHS system will provide personalized access to cultural heritage collections. It consists of subsystems for data integration, user profiles, path authoring, content presentation, information retrieval, recommendations, and a web service API. A hybrid data architecture is proposed using both a relational database and triplestore. The methodology used agile iterative development and evaluated design alternatives. Requirements compliance and roles/responsibilities of partners are defined. Technical details of each subsystem and security, operations and standards are also covered. The architecture is designed to prototype new paradigms for indexing, enrichment and accessing large digital libraries like Europeana.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Aletras, Nikolaos and Stevenson, Mark (2013) "Evaluating Topic Coherence Using Distributional Semantics” (IWCS 2013)
1. Evaluating Topic Coherence Using Distributional Semantics
Nikolaos Aletras
Mark Stevenson
Department of Computer Science
University of Sheffield
Regent Court, 211 Portobello
Sheffield, S1 4DP, UK
{n.aletras, m.stevenson}@dcs.shef.ac.uk
Abstract
This paper introduces distributional semantic similarity methods for automatically measuring the
coherence of a set of words generated by a topic model. We construct a semantic space to represent
each topic word by making use of Wikipedia as a reference corpus to identify context features and
collect frequencies. Relatedness between topic words and context features is measured using variants
of Pointwise Mutual Information (PMI). Topic coherence is determined by measuring the distance
between these vectors computed using a variety of metrics. Evaluation on three data sets shows that
the distributional-based measures outperform the state-of-the-art approach for this task.
1
Introduction
Topic modelling is a popular statistical method for (soft) clustering documents (Blei et al., 2003; Deerwester et al., 1990; Hofmann, 1999). Latent Dirichlet Allocation (LDA) (Blei et al., 2003), one type
of topic model, has been widely used in NLP and applied to a range of tasks including word sense disambiguation (Boyd-Graber et al., 2007), multi-document summarisation (Haghighi and Vanderwende,
2009) and generation of comparable corpora (Preiss, 2012).
A variety of approaches has been proposed to evaluate the topics generated by these models. The
first to be explored were extrinsic methods, measuring the performance achieved by a model in a specific
task or using statistical methods. For example, topic models have been evaluated by measuring their
accuracy for information retrieval (Wei and Croft, 2006). Statistical methods have also been applied to
measure the predictive likelihood of a topic model in held-out documents by computing their perplexity.
Wallach et al. (2009) gives a detailed description of such statistical metrics.
However, these approaches do not provide any information about how interpretable the topics are to
humans. Figure 1 shows some example topics generated by a topic model. The first three topics appear
quite coherent, all the terms in each topic are associated with a common theme. On the other hand,
it is difficult to identify a coherent theme connecting all of the words in topics 4 and 5. These topics
are difficult to interpret and could be considered as “junk” topics. Interpretable topics are important
in applications such as visualisation of document collections (Chaney and Blei, 2012; Newman et al.,
2010a), where automatically generated topics are used to provide an overview of the collection and the
top-n words in each topic used to represent it.
Chang et al. (2009) showed that humans find topics generated by models with high predictive likelihood to be less coherent than topics generated from others with lower predictive likelihood. Following
Chang’s findings, recent work on evaluation of topic models has been focused on automatically measuring the coherence of generated topics by comparing them against human judgements (Mimno et al.,
2011; Newman et al., 2010b). Newman et al. (2010b) define topic coherence as the average semantic
relatedness between topic words and report the best correlation with humans using the Pointwise Mutual
Information (PMI) between topic words in Wikipedia.
2. 1: oil, louisiana, coast, gulf, orleans, spill, state, fisherman, fishing, seafood
2: north, kim, korea, korean, jong, south, il, official, party, son
3: model, wheel, engine, system, drive, front, vehicle, rear, speed, power
4: drink, alcohol, indonesia, drinking, indonesian, four, nokia, beverage, mcdonald, caffeine
5: privacy, andrews, elli, alexander, burke, zoo, information, chung, user, regan
Figure 1: A sample of topics generated by a topic model over a corpus of news articles. Topics are
represented by top-n most probable words.
Following this direction, we explore methods for automatically determining the coherence of topics.
We propose a novel approach for measuring topic coherence based on the distributional hypothesis which
states that words with similar meanings tend to occur in similar context (Harris, 1954). Wikipedia is
used as a reference corpus to create a distributional semantic model (Pad´ and Lapata, 2003; Turney and
o
Pantel, 2010). Each topic word is represented as a bag of highly co-occurring context words that are
weighted using either PMI or a normalised version of PMI (NPMI). We also explore creating the vector
space using differing numbers of context terms. All methods are evaluated by measuring correlation with
humans on three different sets of topics. Results indicating that measures on the fuller vector space are
comparable to the state-of-the-art proposed by Newman et al. (2010b), while performance consistently
improves using a reduced vector space.
The remainder of this article is organised as follows. Section 2 presents background work related
to topic coherence evaluation. Section 3 describes the distributional methods for measuring topic coherence. Section 4 explains the experimental set-up used for evaluation. Our results are described in
Section 5 and the conclusions in Section 6.
2
Related work
Andrzejewski et al. (2009) proposed a method for generating coherent topics which used a mixture of
Dirichlet distributions to incorporate domain knowledge. Their approach prefers words that have similar
probability (high or low) within all topics and rejects words that have different probabilities across topics.
AlSumait et al. (2009) describe the first attempt to automatically evaluate topics inferred from topic
models. Three criteria are applied to identify junk or insignificant topics. Those criteria are in the
form of probability distributions over the highest probability words. For example, topics in which the
probability mass is distributed approximately equally across all words are considered likely to be difficult
to interpret.
Newman et al. (2010b) also focused on methods for measuring the semantic coherence of topics. The
main contribution of this work is to propose a measure for the automatic evaluation of topic semantic
coherence which has been shown to be highly correlated with human evaluation. It is assumed that a
topic is coherent if all or the most of its words are related. Results showed that word relatedness is
better predicted using the distribution-based Pointwise Mutual Information (PMI) of words rather than
knowledge-based measures.
The method using PMI proposed by Newman et al. (2010b) relies on co-occurrences of words in
an external reference source such as Wikipedia for automatic evaluation of topic quality. Mimno et al.
(2011) showed that available co-document frequency of words in the training corpus can be used to
measure semantic coherence. Topic coherence is defined as the sum of the log ratio between co-document
frequency and the document frequency for the N most probable words in a topic. The intuition behind
this metric is that the co-occurrence of words within documents in the corpus can indicate semantic
relatedness.
Musat et al. (2011) associated words in a topic with WordNet concepts thereby creating topical
subtrees. They rely on WordNet’s hierarchical structure to find a common concept that best describes as
many words as possible. It is assumed that the higher the coverage and specificity of a topical subtree,
3. the more semantically coherent the topic. Experimental results showed high agreement with humans in
the word intrusion task, in contrast to Newman et al. (2010b) who concluded that WordNet is not useful
for topic evaluation.
Recent work by Ramirez et al. (2012) analyses and evaluates the semantic coherence of the results
obtained by topic models rather than the semantic coherence of the inferred topics. Each topic model
is treated as a partition of document-topic associations. Results are evaluated using metrics for cluster
comparison.
3
Measuring Topic Coherence
Let T = {w1 , w2 , ..., wn } be a topic generated from a topic model which is represented by its top-n most
probable words. Newman et al. (2010b) assume that the higher the average pairwise similarity between
words in T , the more coherent the topic. Given a symmetric word similarity measure, Sim(wi , wj ), they
define coherence as follows:
P
1in 1 Sim(wi , wj )
i+1jn
✓ ◆
CoherenceSim (T ) =
(1)
n
2
where wi , wj 2 T .
3.1
Distributional Methods
We propose a novel method for determining topic coherence based on using distributional similarity
between the top-n words in the topic. Each topic word is represented as a vector in a semantic space. Let
w1 , w2 , ..., wn be the vectors which represent the top n most probable words in the topic. Also, assume
~ ~
~
that each vector consists of N elements and wij is the jth element of vector wi . Then the similarity
~
~
between the words, and therefore cohesion of the topic, can be computed using the following measures
(Curran, 2003; Grefenstette, 1994):
• The cosine of the angles between the vectors:
Simcos (wi , wj ) =
~ ~
• The Dice coefficient:
• The Jaccard coefficient:
wi · wj
~ ~
kwi kkwj k
~
~
P
2 ⇥ N min(wik , wjk )
~ ~
k=1
SimDice (wi , wj ) =
PN
~
~
k=1 (wik + wjk )
PN
k=1
SimJaccard (wi , wj ) = PN
min(wik , wjk )
~ ~
~ ~
k=1 max(wik , wjk )
(2)
(3)
(4)
Each of these measures estimates the distance between a pair of topic words and can be substituted into
equation 1 to produce a topic cohesion measure based on distributional semantics.
Alternatively, the cohesion of a set of topic words can be estimated with a single measure by computing the average distance between each topic word and the centroid:
P
simcos (Tc , t)
Simcentroid = t2T
(5)
n
where Tc is the centroid of the vectors for topic T . For the experiments reported in this paper the distance
of each vector to the centroid is computed using the cosine measure.
4. 3.2
Constructing the Semantic Space
Vectors representing the topic words are constructed from a semantic space consisting of information
about word co-occurrence. The semantic space was created using Wikipedia1 as a reference corpus and
a window of ± 5 words2 .
3.2.1
Weighting Vectors
Using the co-occurrence information to generate vectors directly does not produce good results so the
vectors are weighted using two approaches.
For the first, PMI, the pointwise mutual information for each term in the context is used rather than
the raw co-occurrence count. PMI is computed as follows:
PMI(wi , wj ) = log2
p(wi , wj )
p(wi )p(wj )
(6)
Note that this application of PMI for topic cohesion is different from one previously reported by Newman
et al. (2010b) since we use PMI to weight vectors rather than to compute a similarity score between pairs
of words.
In addition, vectors are also weighted using NPMI (Normalised PMI). This is an extension of PMI
that has been used for collocation extraction (Bouma, 2009) and is computed as follows:
NPMI(wi , wj ) =
P M I(wi , wj )
log(p(wi , wj ))
(7)
Finally, we introduce which is a parameter to assign more emphasis on context features with high
PMI (or NPMI) values with a topic word. Vectors are weighted using P M I(wi , fj ) or N P M I(wi , fj )
where wi is a topic word and fj is a context feature. For all of our experiments we set = 2 which was
found to produce the best results.
3.2.2
Reducing the Basis
Including all co-occurring terms in the vectors leads to a high dimensional space. We also experimented
with two approaches to reducing the number of terms to form a semantic space with smaller basis.
Firstly, following Islam and Inkpen (2006), a Reduced Semantic Space is created by choosing the wi
most related context features for each topic word wi :
wi
= log(c(wi ))
2 (log2 (m))
(8)
where is a parameter for adjusting the number of features for each word and m is the size of the corpus.
Varying the value of did not effect performance for values above 1. This parameter was set of 3 for
the results reported here. In addition a frequency cut-off of 20 was also applied. In addition, a smaller
semantic space was created by considering only topic words as context features, leading to n features for
each topic word. This is referred to as the Topic Word Space.
4
Experimental Set-up
4.1
Data
To the best of our knowledge, there are no standard data sets for evaluating topic coherence. Therefore
we have developed one for this study which we have made publicly available3 . A total of 300 topics are
1
http://dumps.wikimedia.org/enwiki/20120104/
We also experimented with different lengths of context windows
3
The data set can be downloaded from http://staffwww.dcs.shef.ac.uk/people/N.Aletras/
resources/TopicCoherence300.tar.gz
2
5. generated by running LDA over three different document collections:
• NYT: 47,229 New York Times news articles published between May and December 2010 from
the GigaWord corpus. We generated 200 topics and randomly selected 100.
• 20NG: The 20 News Group Data Collection4 (20NG), a set of 20,000 newsgroup emails organised into 20 different subjects (e.g. sports, computers, politics). Each topic has 1,000 documents
associated with it. 100 topics were generated for this data set.
• Genomics: 30,000 scientific articles published in 49 journals from MEDLINE, originally used in
the TREC-Genomics Track5 . We generated 200 topics and randomly selected 100.
All document were pre-processed by removing stop words and lemmatising. Topics are generated
1
using gensim6 with hyperparameters (↵, ) set to num of topics . Each topic is represented by its 10 most
probable words.
4.2
Human Evaluation of Topic Coherence
Human judgements of topic coherence were collected through a crowdsourcing platform, CrowdFlower7 .
Participants were presented with 10 word sets, each of which represents a topic. They asked to judge
topic coherence on a 3-point Likert scale from 1-3, where 1 denotes a “Useless” topic (i.e. words appear
random and unrelated to each other), 2 denotes “Average” quality (i.e. some of the topic words are
coherent and interpretable but others are not), and 3 denotes a “Useful” topic (i.e. one that is semantically
coherent, meaningful and interpretable). Each participant was asked to judge up to 100 topics from a
single collection. The average response for each topic was calculated as the coherency score for the
gold-standard.
To ensure reliability and avoid random answers in the survey, we used a number of questions with
predefined answer (either totally random words as topics or obvious topics such as week days). Annotations from participants that failed to answer these questions correctly were removed.
We run three surveys, one for each topic collection of 100 topics. The total number of filtered
responses obtained for the NYT dataset was 1, 778 from 26 participants, while for the 20NG dataset
we collected 1, 707 answers from 24 participants. The participants were recruited by a broadcast email
sent to all academic staff and graduate students in our institution. For the Genomics dataset the emails
were sent only to members of the medical school and biomedical engineering departments. We collected
1, 050 judgements from 12 participants for this data set.
Inter-annotator agreement (IAA) is measured as the average of the Spearman correlation between the
set of scores of each survey respondent and the average of the other respondents’ scores. The IAA in the
three surveys is 0.70, 0.64 and 0.54 for NYT, 20NG and Genomics respectively.
5
Results
Table 1 shows the results obtained for all of the methods on the three datasets. Performance of each
method is measured as the average Spearman correlation with human judgements. The top row of each
table shows the result using the average PMI approach (Newman et al., 2010b) while the next two rows
show the results obtained by substituting PMI with NPMI and the method proposed by Mimno et al.
(2011). The main part of each table shows performance using the approaches described in Section 3 using
various combinations of methods for constructing the semantic space and determining the similarity
between vectors.
4
http://people.csail.mit.edu/jrennie/20Newsgroups
http://ir.ohsu.edu/genomics
6
http://radimrehurek.com/gensim
7
http://crowdflower.com
5
6. NYT
Newman et al. (2010b) 0.71
Average NPMI 0.74
Mimno et al. (2011) -0.39
Reduced Semantic Space
PMI
NPMI
Cosine
0.69
0.68
Dice
0.63
0.62
Jaccard
0.63
0.61
Centroid
0.67
0.67
Topic Words Space
PMI
NPMI
Cosine
0.76
0.75
Dice
0.68
0.71
Jaccard
0.69
0.72
Centroid
0.76
0.75
20NG
Newman et al. (2010b) 0.73
Average NPMI 0.76
Mimno et al. (2011) 0.34
Reduced Semantic Space
PMI
NPMI
Cosine
0.78
0.79
Dice
0.77
0.78
Jaccard
0.77
0.78
Centroid
0.77
0.78
Topic Words Space
PMI
NPMI
Cosine
0.79
0.8
Dice
0.79
0.8
Jaccard
0.8
0.8
Centroid
0.78
0.79
Genomics
Newman et al. (2010b) 0.73
Average NPMI 0.76
Mimno et al. (2011)
-0.4
Reduced Semantic Space
PMI
NPMI
Cosine
0.74
0.73
Dice
0.69
0.68
Jaccard
0.69
0.76
Centroid
0.73
0.71
Topic Words Space
PMI
NPMI
Cosine
0.8
0.8
Dice
0.79
0.8
Jaccard
0.8
0.8
Centroid
0.8
0.8
Table 1: Performance of methods for measuring topic coherence (Spearman Rank correlation with human
judgements).
Using the average PMI between topic words correlates well with human judgements, 0.71 for NYT,
0.73 for 20NG and 0.75 for Genomics confirming results reported by Newman et al. (2010b). However, NPMI performs better than PMI, with an improvement in correlation of 0.03 for all datasets. The
improvement is down to the fact that NPMI reduces the impact of low frequency counts in word cooccurrences and therefore uses more reliable estimates (Bouma, 2009).
On the other hand, the method proposed by Mimno et al. (2011) does not correlate well with human
judgements, (-0.39 for NYT, 0.34 for 20NG and -0.4 for Genomics) which is the lowest performance of
all of the methods tested. This demonstrates that while co-document frequency helps to generate more
coherent topics (Mimno et al., 2011), it is sensitive to the size of the collection.
Results obtained using the reduced semantic space and PMI are lower than the average PMI and
NPMI approaches for the NYT and Genomics data sets. For the 20NG dataset the results are higher
then the average PMI and NPMI using these approaches. The difference in relative performance is down
to the nature of these corpora. The words found in topics in the NYT and Genomics datasets are often
7. Topic Terms
Top-3
family wife died son father daughter life became mother born
election vote voter ballot state candidate voting percent party result
show television tv news network medium fox cable channel series
Bottom-3
lennon circus rum whiskey lombardi spirits ranch idol make vineyard
privacy andrews elli alexander burke zoo information chung user regan
twitter board tweet followers conroy halloween kay hands emi post
Human Rating
2.63
3
2.82
1.93
1.25
1.53
Figure 2: Top-3 and bottom-3 ranked topics using Topic Word Space in NYT together with human
ratings.
polysemous or collocate with terms which become context features. For example, one of the top context
features of the word “coast” is “ivory” (from the country). However, that feature does not exist for terms
that are related to “coast”, such as “beach” or “sea”. The majority of topics generated from 20NG contain
meaningless terms due to the noisy nature of the dataset (emails) but these do not suffer from the same
problems with ambiguity and prove to be useful for comparing meaning when formed into the semantic
space.
Similar results are obtained for the reduced semantic space using NPMI as the association measure.
Results in NYT and Genomics are normally 0.01 lower while for 20NG are 0.01 higher for the majority
of the methods. This demonstrates that weighting co-occurrence vectors using NPMI produces little
improvement over using PMI, despite the fact NPMI has better performance when the average similarity
between each pair of topic terms is computed.
When the topic word space is used there is a consistent improvement in performance compared to
the average PMI (Newman et al., 2010b) and NPMI approaches. More specifically, cosine similarity
using PMI is consistently higher (0.05-0.06) than average PMI for all datasets and 0.02 to 0.04 higher
than average NPMI (0.76, 0.79, 0.8 for NYT, 20NG and Genomics respectively). One reason for this
improvement in performance is that the noise caused by polysemy and high dimensionality of the context
features of the topic words is reduced. Moreover, cosine similarity scores in the reduced semantic space
are higher than average PMI and NPMI in all of the datasets, demonstrating that vector-based representation of the topic words is better than computing their average relatedness. Table 2 shows the top-3 and
bottom-3 ranked topics in NYT together with human ratings.
Another interesting finding is that the cosine metric produces better estimates of topic coherency
compared to Dice and Jaccard in the majority of cases, with the exception of 20NG in reduced semantic space using PMI. Furthermore, similarity to the topic centroid achieves performance comparable to
cosine.
6
Conclusions
This paper explored distributional semantic similarity methods for automatically measuring the coherence of sets of words generated by topic models. Representing topic words as vectors of context features
and then applying similarity metrics on vectors was found to produce reliable estimates of topic coherence. In particular, using a semantic space that consisted of only the topic words as context features
produced the best results and consistently outperforms previously proposed methods for the task.
Semantic space representations have appealing characteristics for future work on tasks related to
topic models. The vectors used to represent topic words contain co-occurring terms that could be used
for topic labelling (Lau et al., 2011). In addition, tasks such as determining topic similarity (e.g. to
identify similar topics) could naturally be explored using these representations for topics.
8. Acknowledgments
The research leading to these results was carried out as part of the PATHS project (http://paths-project.eu)
funded by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant
agreement no. 270082.
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