A presentation of our project done as part of Information Retrieval and Extraction course (2014). The project deals with clustering similar documents and extracting topics from it.
History of Types in Elasticsearch
Why They are being removed
How to migrate from old ES version using multiple types per index to the new version with one type per index or custom type fields
Introduction to Text Mining and Topic ModellingDavid Paule
A brief introduction to Text Mining and Topic Modelling given at the Urban Big Data Centre (University of Glasgow).
Want to know more? Visit my website davidpaule.es
Query Translation for Data Sources with Heterogeneous Content Semantics Jie Bao
The document discusses query translation for data sources with heterogeneous content semantics. It proposes using ontology-extended data sources to make explicit the implicit ontologies associated with data. The key aspects covered include translating queries between different data content ontologies using conversion functions and interoperation constraints to ensure sound, complete, or exact translations.
This document discusses various techniques for information retrieval (IR), including global and local methods. Global methods reformulate queries while local methods are relative to initial search results. Local methods discussed include relevance feedback, probabilistic relevance feedback, and indirect feedback. The Rocchio algorithm incorporates relevance feedback into the vector space model using cosine similarity. Naive Bayes classification and support vector machines are also covered as techniques for text classification.
Using Text Comprehension Model for Learning Concepts, Context, and Topic of...Kent State University
Concepts in web ontologies help machines to un-
derstand data through the meanings they hold. Furthermore,
learning contexts and topics of web documents also have helped
in better semantic-oriented structuring and retrieval of data on
the web. In this short paper we present a novel approach for
domain-independent open learning of domain concepts, context
and topic of any given web document. Our approach is based on a
computational version of the Construction-Integration (CI) model
of text comprehension. Our proposed system mimics the way
humans learn the meanings of textual units and identify domain
concepts, contexts and topics in the form of semantic networks.
We apply our system on a number of web documents with a
range of topics and domains. The resulting semantic networks
provide a quantitative and qualitative insights into the nature of
the given web documents.
This document discusses HTML, CSS, and data structures. It covers basic HTML and CSS concepts like the box model and selectors. It explains that data can take different structures like sequences, trees, and tables. CSS uses selectors to select and transform elements in the DOM tree based on patterns, addressing elements to apply styles. The key idea is using selectors to pick elements and apply rules to control appearance and behavior. Exercises are included to practice marking up a text with HTML and styling it with CSS. An assignment is given to create a simple multi-page website with styling.
History of Types in Elasticsearch
Why They are being removed
How to migrate from old ES version using multiple types per index to the new version with one type per index or custom type fields
Introduction to Text Mining and Topic ModellingDavid Paule
A brief introduction to Text Mining and Topic Modelling given at the Urban Big Data Centre (University of Glasgow).
Want to know more? Visit my website davidpaule.es
Query Translation for Data Sources with Heterogeneous Content Semantics Jie Bao
The document discusses query translation for data sources with heterogeneous content semantics. It proposes using ontology-extended data sources to make explicit the implicit ontologies associated with data. The key aspects covered include translating queries between different data content ontologies using conversion functions and interoperation constraints to ensure sound, complete, or exact translations.
This document discusses various techniques for information retrieval (IR), including global and local methods. Global methods reformulate queries while local methods are relative to initial search results. Local methods discussed include relevance feedback, probabilistic relevance feedback, and indirect feedback. The Rocchio algorithm incorporates relevance feedback into the vector space model using cosine similarity. Naive Bayes classification and support vector machines are also covered as techniques for text classification.
Using Text Comprehension Model for Learning Concepts, Context, and Topic of...Kent State University
Concepts in web ontologies help machines to un-
derstand data through the meanings they hold. Furthermore,
learning contexts and topics of web documents also have helped
in better semantic-oriented structuring and retrieval of data on
the web. In this short paper we present a novel approach for
domain-independent open learning of domain concepts, context
and topic of any given web document. Our approach is based on a
computational version of the Construction-Integration (CI) model
of text comprehension. Our proposed system mimics the way
humans learn the meanings of textual units and identify domain
concepts, contexts and topics in the form of semantic networks.
We apply our system on a number of web documents with a
range of topics and domains. The resulting semantic networks
provide a quantitative and qualitative insights into the nature of
the given web documents.
This document discusses HTML, CSS, and data structures. It covers basic HTML and CSS concepts like the box model and selectors. It explains that data can take different structures like sequences, trees, and tables. CSS uses selectors to select and transform elements in the DOM tree based on patterns, addressing elements to apply styles. The key idea is using selectors to pick elements and apply rules to control appearance and behavior. Exercises are included to practice marking up a text with HTML and styling it with CSS. An assignment is given to create a simple multi-page website with styling.
The document discusses a study that trained a GPT-2 model to generate contextual definitions for words based on the provided context. The model was trained on a new dataset containing definition and context pairs from various sources. It was evaluated through surveys where human raters assessed definitions generated by the model for short and long contexts, as well as real human-generated definitions. The results found that while the model performed significantly better at generating definitions for short contexts compared to long ones, human-generated definitions were still significantly more accurate. Areas for improvement included reducing fluctuations depending on context and better interpreting some contexts.
Object-Oriented Writing: augmented writing for creating coherent and argument...Seong-Young Her
The document proposes an "Object-Oriented Writing" (OOW) approach to augment writing, especially for philosophy. It draws analogies between object-oriented programming principles and organizing writing. The OOW approach structures arguments, assets, and products in a formalized ontology to improve coherence, reduce redundancy, and enable collaboration. A proposed OOW tool would standardize information from diverse sources into machine-readable formats linked by metadata. It aims to help generate, organize, and share philosophical work. Empirical research prototyping the tool on existing texts could help refine and evaluate the OOW approach.
The document discusses logical database design principles including defining entities, attributes, relationships, and naming conventions. It describes entity-relationship diagrams and the three types of relationships: one-to-one, one-to-many, and many-to-many. Many-to-many relationships must be resolved into two one-to-many relationships with a linking table. The document also introduces the concept of cardinality which specifies the minimum and maximum number of relationships between entities.
SAX (Simple API for XML) and DOM (Document Object Model) both provide programmatic access to XML documents, but differ in their approaches. SAX processes XML documents as a stream of parsing events rather than building an in-memory tree. It is great for linear processing of large XML documents. Unlike DOM, SAX can only be used for parsing existing documents in a stream, not for generating documents. SAX notifies a client program through events as it reads an XML document sequentially.
Order out of Chaos: Construction of Knowledge Models from PDF TextbooksIsaac Alpizar-Chacon
Textbooks are educational documents created, structured and formatted by domain experts with the main purpose to explain the knowledge in the domain to a novice. Authors use their understanding of the domain when structuring and formatting the content of a textbook to facilitate this explanation. As a result, the formatting and structural elements of textbooks carry the elements of domain knowledge implicitly encoded by their authors. Our paper presents an extendable approach towards automated extraction of this knowledge from textbooks taking into account their formatting rules and internal structure. We focus on PDF as the most common textbook representation format; however, the overall method is applicable to other formats as well. The evaluation experiments examine the accuracy of the approach, as well as the pragmatic quality of the obtained knowledge models using one of their possible applications --- semantic linking of textbooks in the same domain. The results indicate high accuracy of model construction on symbolic, syntactic and structural levels across textbooks and domains, and demonstrate the added value of the extracted models on the semantic level.
Presented at Document Engineering 2020
Cross-domain Document Retrieval: Matching between Conversational and Formal W...Jinho Choi
This paper challenges a cross-genre document retrieval task, where the queries are in formal writing and the target documents are in conversational writing. In this task, a query, is a sentence extracted from either a summary or a plot of an episode in a TV show, and the target document consists of transcripts from the corresponding episode. To establish a strong baseline, we employ the current state-of-the-art search engine to perform document retrieval on the dataset collected for this work. We then introduce a structure reranking approach to improve the initial ranking by utilizing syntactic and semantic structures generated by NLP tools. Our evaluation shows an improvement of more than 4% when the structure reranking is applied, which is very promising.
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.
There are many examples of text-based documents (all in ‘electronic’ format…)
e-mails, corporate Web pages, customer surveys, résumés, medical records, DNA sequences, technical papers, incident reports, news stories and more…
Not enough time or patience to read
Can we extract the most vital kernels of information…
So, we wish to find a way to gain knowledge (in summarised form) from all that text, without reading or examining them fully first…!
Some others (e.g. DNA seq.) are hard to comprehend!
StaTIX - Statistical Type Inference on Linked DataArtem Lutov
StaTIX - Statistical Type Inference on Linked Data, presented in BigData 2018, Special Session on Intelligent Data Mining
https://github.com/eXascaleInfolab/StaTIX
DOM,XML,DOCUMENT OBJECT MODEL,DOCUMENT,OBJECT,MODEL,API,XML API,INTERFACE,XML INTERFACE,DOM INTERFACE,DOM IN XML,DOM LEVELS,DOM CORE,XML PARSER,XML AND WEB SERVICES,
This document discusses XML stylesheet language (XSL) which has two languages - XSL Transformation Language (XSLT) to convert XML documents to other formats, and XSL Formatting Objects Language (XSL-FO) to describe presentation of XML documents. It provides an example using an XML book document and XSL stylesheet to display book details by applying XSLT template rules and retrieving element values using <xsl:value-of> tags.
localeikki’s overall goal is to help people be active wherever they travel. We're creating a platform that allows individuals to coordinate their recreation and travel plans through local recommendations from like-minded folks and intelligence prompts based on travel patterns and recreation choices.
This document summarizes trends in non-motorized transportation for children and youth in urban areas globally. It finds that non-motorized transportation, especially walking, remains an important mode for many children's trips to school, with rates ranging from 23-53% in studies from various cities. However, some developed countries show a decreasing trend in walking and cycling to school over time. Factors like distance, traffic safety, and infrastructure affect children's transportation choices. Overall data on youth transportation is limited but non-motorized modes are likely still significant given age restrictions on driving.
The document discusses an app called Localeikki that helps travelers find local recreation activities like running trails when traveling. It provides statistics on the app's growth and user base. The company is seeking to raise $300,000 and has already raised $135,000. It was acquired by UnderArmour for $150 million and aims to leverage the intersection of travel and recreation trends.
7 facts that parents have taught us
Kolibree surveyed hundreds of parents in the USA and France to find out how they feel about their children's dental health routines.
Adobe Illustrator Software course and Tutorials of Pathfinder Palette by Bapu Graphics. Multimedia Computer Education. Covering all commands with visual figures of Pathfinder palette with their respective results and names.
Kolibree 2014 - The World's First Connected Electric ToothbrushKolibree
Kolibree is the World’s First Connected Toothbrush. Connected via Bluetooth to any iOS or Android device get feedback to improve your brushing, use gaming to engage kids and spend less money on expensive but avoidable dental procedures.
The document discusses a study that trained a GPT-2 model to generate contextual definitions for words based on the provided context. The model was trained on a new dataset containing definition and context pairs from various sources. It was evaluated through surveys where human raters assessed definitions generated by the model for short and long contexts, as well as real human-generated definitions. The results found that while the model performed significantly better at generating definitions for short contexts compared to long ones, human-generated definitions were still significantly more accurate. Areas for improvement included reducing fluctuations depending on context and better interpreting some contexts.
Object-Oriented Writing: augmented writing for creating coherent and argument...Seong-Young Her
The document proposes an "Object-Oriented Writing" (OOW) approach to augment writing, especially for philosophy. It draws analogies between object-oriented programming principles and organizing writing. The OOW approach structures arguments, assets, and products in a formalized ontology to improve coherence, reduce redundancy, and enable collaboration. A proposed OOW tool would standardize information from diverse sources into machine-readable formats linked by metadata. It aims to help generate, organize, and share philosophical work. Empirical research prototyping the tool on existing texts could help refine and evaluate the OOW approach.
The document discusses logical database design principles including defining entities, attributes, relationships, and naming conventions. It describes entity-relationship diagrams and the three types of relationships: one-to-one, one-to-many, and many-to-many. Many-to-many relationships must be resolved into two one-to-many relationships with a linking table. The document also introduces the concept of cardinality which specifies the minimum and maximum number of relationships between entities.
SAX (Simple API for XML) and DOM (Document Object Model) both provide programmatic access to XML documents, but differ in their approaches. SAX processes XML documents as a stream of parsing events rather than building an in-memory tree. It is great for linear processing of large XML documents. Unlike DOM, SAX can only be used for parsing existing documents in a stream, not for generating documents. SAX notifies a client program through events as it reads an XML document sequentially.
Order out of Chaos: Construction of Knowledge Models from PDF TextbooksIsaac Alpizar-Chacon
Textbooks are educational documents created, structured and formatted by domain experts with the main purpose to explain the knowledge in the domain to a novice. Authors use their understanding of the domain when structuring and formatting the content of a textbook to facilitate this explanation. As a result, the formatting and structural elements of textbooks carry the elements of domain knowledge implicitly encoded by their authors. Our paper presents an extendable approach towards automated extraction of this knowledge from textbooks taking into account their formatting rules and internal structure. We focus on PDF as the most common textbook representation format; however, the overall method is applicable to other formats as well. The evaluation experiments examine the accuracy of the approach, as well as the pragmatic quality of the obtained knowledge models using one of their possible applications --- semantic linking of textbooks in the same domain. The results indicate high accuracy of model construction on symbolic, syntactic and structural levels across textbooks and domains, and demonstrate the added value of the extracted models on the semantic level.
Presented at Document Engineering 2020
Cross-domain Document Retrieval: Matching between Conversational and Formal W...Jinho Choi
This paper challenges a cross-genre document retrieval task, where the queries are in formal writing and the target documents are in conversational writing. In this task, a query, is a sentence extracted from either a summary or a plot of an episode in a TV show, and the target document consists of transcripts from the corresponding episode. To establish a strong baseline, we employ the current state-of-the-art search engine to perform document retrieval on the dataset collected for this work. We then introduce a structure reranking approach to improve the initial ranking by utilizing syntactic and semantic structures generated by NLP tools. Our evaluation shows an improvement of more than 4% when the structure reranking is applied, which is very promising.
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.
There are many examples of text-based documents (all in ‘electronic’ format…)
e-mails, corporate Web pages, customer surveys, résumés, medical records, DNA sequences, technical papers, incident reports, news stories and more…
Not enough time or patience to read
Can we extract the most vital kernels of information…
So, we wish to find a way to gain knowledge (in summarised form) from all that text, without reading or examining them fully first…!
Some others (e.g. DNA seq.) are hard to comprehend!
StaTIX - Statistical Type Inference on Linked DataArtem Lutov
StaTIX - Statistical Type Inference on Linked Data, presented in BigData 2018, Special Session on Intelligent Data Mining
https://github.com/eXascaleInfolab/StaTIX
DOM,XML,DOCUMENT OBJECT MODEL,DOCUMENT,OBJECT,MODEL,API,XML API,INTERFACE,XML INTERFACE,DOM INTERFACE,DOM IN XML,DOM LEVELS,DOM CORE,XML PARSER,XML AND WEB SERVICES,
This document discusses XML stylesheet language (XSL) which has two languages - XSL Transformation Language (XSLT) to convert XML documents to other formats, and XSL Formatting Objects Language (XSL-FO) to describe presentation of XML documents. It provides an example using an XML book document and XSL stylesheet to display book details by applying XSLT template rules and retrieving element values using <xsl:value-of> tags.
localeikki’s overall goal is to help people be active wherever they travel. We're creating a platform that allows individuals to coordinate their recreation and travel plans through local recommendations from like-minded folks and intelligence prompts based on travel patterns and recreation choices.
This document summarizes trends in non-motorized transportation for children and youth in urban areas globally. It finds that non-motorized transportation, especially walking, remains an important mode for many children's trips to school, with rates ranging from 23-53% in studies from various cities. However, some developed countries show a decreasing trend in walking and cycling to school over time. Factors like distance, traffic safety, and infrastructure affect children's transportation choices. Overall data on youth transportation is limited but non-motorized modes are likely still significant given age restrictions on driving.
The document discusses an app called Localeikki that helps travelers find local recreation activities like running trails when traveling. It provides statistics on the app's growth and user base. The company is seeking to raise $300,000 and has already raised $135,000. It was acquired by UnderArmour for $150 million and aims to leverage the intersection of travel and recreation trends.
7 facts that parents have taught us
Kolibree surveyed hundreds of parents in the USA and France to find out how they feel about their children's dental health routines.
Adobe Illustrator Software course and Tutorials of Pathfinder Palette by Bapu Graphics. Multimedia Computer Education. Covering all commands with visual figures of Pathfinder palette with their respective results and names.
Kolibree 2014 - The World's First Connected Electric ToothbrushKolibree
Kolibree is the World’s First Connected Toothbrush. Connected via Bluetooth to any iOS or Android device get feedback to improve your brushing, use gaming to engage kids and spend less money on expensive but avoidable dental procedures.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Bootstrap course will help you making responsive website with fast and easy way, Responsive websites which can be run in all screen media sizes Like of Tabs, Mobiles or Monitors.
Este documento habla sobre los riesgos para la piel y cómo protegerse. Identifica varios peligros como quemaduras, cortes y dermatitis. Recomienda evaluar los peligros potenciales, usar ropa y equipo de protección adecuados, lavarse la piel después de trabajar y buscar atención médica ante cualquier herida o problema en la piel. El objetivo es concientizar sobre la importancia de proteger la piel.
Adobe Photoshop Tools: Get knowledge about Adobe tools, Photoshop learn Adobe Photoshop from Bapu Graphics the best Graphics and Web Designing institute in Delhi,
This document provides an overview of topic modeling. It defines topic modeling as discovering the thematic structure of a corpus by modeling relationships between words and documents through learned topics. The document introduces Latent Dirichlet Allocation (LDA) as a widely used topic modeling technique. It outlines LDA's generative process and inference methods like Gibbs sampling and variational inference. The document also discusses extensions to LDA, evaluation strategies, open questions, and applications like topic labeling and browsing.
This presentation contains differences between Elasticsearch and relational Databases. Along with that it also has some Glossary Of Elasticsearch and its basic operation.
This document provides an introduction to topic modelling. It discusses how topic modelling can be used to summarize large collections of documents by clustering them into topics. It describes latent Dirichlet allocation (LDA) as a commonly used topic modelling technique that represents documents as mixtures of topics and topics as mixtures of words. The document outlines how LDA works using a generative process and Gibbs sampling. It also discusses other related methods like latent semantic analysis, word2vec, and Ida2vec. Evaluation techniques for topic models like word and topic intrusion are presented.
This presentation was provided by William Mattingly of the Smithsonian Institution, for the sixth session of NISO's 2023 Training Series on Text and Data Mining. Session six, "Text Mining Techniques" was held on Thursday, November 16, 2023.
Discourse Corpra about the subject of semanticsssuseree197e
This document discusses discourse processing and theories of discourse structure. It provides an overview of several theories including Rhetorical Structure Theory (RST), Segmented Discourse Representation Theory (SDRT), and the Penn Discourse Treebank (PDTB). It also discusses concepts like elementary discourse units, coherence relations, and discourse annotation corpora. The document aims to give a general introduction to discourse theories from the perspective of the relations and conceptions that link different units of text.
The document summarizes different techniques for automatic document summarization including extractive and abstractive approaches. It discusses simple techniques like frequency-based methods and cue phrases. Graph-based approaches like TextRank and LexRank that model text as a graph are explained. Linguistic methods involving lexical chains and rhetorical structure are covered. Finally, it summarizes WordNet-based semantic approaches and techniques for evaluating summaries.
EXPERT OPINION AND COHERENCE BASED TOPIC MODELINGijnlc
In this paper, we propose a novel algorithm that rearrange the topic assignment results obtained from topic
modeling algorithms, including NMF and LDA. The effectiveness of the algorithm is measured by how much
the results conform to expert opinion, which is a data structure called TDAG that we defined to represent the
probability that a pair of highly correlated words appear together. In order to make sure that the internal
structure does not get changed too much from the rearrangement, coherence, which is a well known metric
for measuring the effectiveness of topic modeling, is used to control the balance of the internal structure.
We developed two ways to systematically obtain the expert opinion from data, depending on whether the
data has relevant expert writing or not. The final algorithm which takes into account both coherence and
expert opinion is presented. Finally we compare amount of adjustments needed to be done for each topic
modeling method, NMF and LDA.
Machine learning can be used at government levels for applications like spam detection, sentiment analysis, text summarization, and topic modeling. Topic modeling uses algorithms like Latent Dirichlet Allocation to analyze documents and discover hidden topics. For example, LDA treats each document as a mixture of topics and each word's presence as attributable to one of the document's topics. It represents this with a graphical model that visualizes how parameters like document topic distributions relate. Machine learning can also be used for time series forecasting, such as predicting public library visits and book borrowing by reframing it as a supervised learning problem and using data visualization.
Post-conference workshop at tcworld India 2012. Provides background on structured authoring, XML, planning your topics, writing topics, and writing for re-use.
This document discusses text encoding and markup. It introduces XML and the Text Encoding Initiative (TEI), which uses XML to encode scholarly documents. Key points include:
- XML allows users to define their own semantic markup languages and impose interpretive models on texts through schemas like TEI.
- TEI is the dominant language for encoding scholarly texts and primary sources. It allows scholars to select elements to match their areas of interest.
- XML and TEI view texts as ordered hierarchies of content objects (OHCO), representing them as trees. This has advantages like easy processing but also limitations regarding overlaps in logical and physical structure.
- Different representational tools like tables and trees can be used to reconcile textual
Web classification of Digital Libraries using GATE Machine Learning sstose
This document provides an introduction to classifying digital library documents using machine learning with GATE. It discusses how text mining applies natural language processing to analyze textual content and extract knowledge. While humans can understand language intuitively, machines struggle with aspects like context and ambiguity. The document then outlines how natural language preprocessing is used for text classification, including tokenization, stopword removal, and representing documents as bags of words. The goal is to train machine learning algorithms on linguistic features to automatically categorize new documents.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Data Structures and Algorithms (DSA) form the backbone of efficient and optimized software solutions. Whether you’re preparing for coding interviews or aiming to enhance your problem-solving skills, understanding DSA is essential. In this comprehensive guide, we’ll explore the key topics and algorithms in DSA, equipping you with the knowledge to tackle complex programming challenges.
In this series on Data Structures and Algorithms (DSA), we dive deep into each topic, providing a clear understanding of their purpose, implementation, and use cases. These notes serve as a comprehensive resource, covering both fundamental concepts and advanced algorithms.
Preparing for coding interviews? These notes cover a range of algorithms, including popular graph algorithms like Breadth First Search (BFS) and Depth First Search (DFS), shortest path algorithms like Dijkstra’s Algorithm and Bellman-Ford Algorithm, and dynamic programming techniques. By studying these algorithms and understanding their implementation, you’ll be well-prepared to tackle interview questions that require efficient problem-solving skills.
Understanding the efficiency of algorithms is crucial. That’s why we cover Big O notation, enabling you to analyze and compare the time and space complexities of different algorithms.
From foundational data structures like arrays, linked lists, stacks, and queues to advanced concepts like trees, binary search trees, AVL trees, and heaps, these notes provide comprehensive coverage of DSA.
Unlock the power of data structures and algorithms by exploring these notes, which encompass both theory and practical implementation. Enhance your problem-solving skills, optimize your code, and excel in coding interviews.
The document discusses ontology matching, which is the process of finding relationships between entities in different ontologies. It describes various techniques for ontology matching including basic techniques that operate at the element-level or structure-level, as well as classifications of matching techniques based on the type of input used and level of interpretation. The document also provides examples of commonly used methods for ontology matching like string-based, language-based, and structure-based techniques.
This presentation is a briefing of a paper about Networks and Natural Language Processing. It describes many graph based methods and algorithms that help in syntactic parsing, lexical semantics and other applications.
This document presents the Duet model for document ranking. The Duet model uses a combination of local and distributed representations of text to perform both exact and inexact matching of queries to documents. The local model operates on a term interaction matrix to model exact matches, while the distributed model projects text into an embedding space for inexact matching. Results show the Duet model, which combines these approaches, outperforms models using only local or distributed representations. The Duet model benefits from training on large datasets and can effectively handle queries containing rare terms or needing semantic matching.
Digital scholarly editions (DSEs) facilitate collaboration, dissemination of resources, and new analyses. Digital epigraphy uses EpiDoc, a TEI subset, to encode inscriptions. EpiDoc divides texts into conventional parts and provides tools. XML describes text structures with tags, separating content from presentation. This allows flexible outputs from a single source and reuse across projects.
The document discusses various techniques for dimensionality reduction and analysis of text data, including latent semantic indexing (LSI), locality preserving indexing (LPI), and probabilistic latent semantic analysis (PLSA). LSI uses singular value decomposition to project documents into a lower-dimensional space while minimizing reconstruction error. LPI aims to preserve local neighborhood structures between similar documents. PLSA models documents as mixtures of underlying latent themes characterized by multinomial word distributions.
Similar to Hierarchical Topic Detection and Representation (20)
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
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1. Hierarchical Topic Detection and Representation
Yash Vadalia (201001015)
Raj Mehta (201305504)
Lalit M (201101189)
Ashutosh Borkar (201101002)
2. Introduction
● Huge volume of news/information.
● Automatic processing of information to keep up with latest updates.
● Documents with similar stories are clustered together.
● Topics extracted from these clusters.
● Applications: Searching, topic based document suggestion.
4. Parsing
● Corpus: Real news dataset (link).
● Unstructured data makes information extraction difficult.
● Data has huge amount of noise.
○ html tags
○ non-printable characters
5. ...continued
● Process the raw data and remove noise (HTML tags, comments, etc).
● Segment each document into sentences and further into words/tokens.
● Stop words removal and Stemming.
● Tag each token with the right parts of speech (POS).
● Store the tag and frequency of all nouns and verbs (document vector)
6. Document Similarity
● Document similarity: Cosine similarity of document vectors.
● Higher the similarity, more the probability of having similar topic
● Wt
represents weight of a word and is given by
7. Cluster Similarity
● Various linkage criteria are available for finding similarity between
clusters:
○ Single Linkage
○ Complete Linkage
○ Mean Linkage
○ Centroid Linkage
○ Minimum Energy etc
● Mean Linkage is prefered over other since it reduces the effect of chaining.
8. Clustering
● Agglomerative hierarchical clustering
○ Consider each document as single cluster
○ Find most (max) similar pair of clusters to merge
○ Merge into single cluster
○ Repeat
● Each iteration reduces the number of cluster by one.
● Termination
○ Either the maximum similarity goes below a threshold
○ Requisite number of clusters formed.
9. Topic Extraction
● Used TF-IDF and parsimonious model to weigh terms to get the most
relevant topics.
● Parsimonious Model
10. ...continued
● Words having less weight are ignored and ones with maximum weight are
considered as topic for that cluster.
● Instead of all kinds of word, processing specific parts of speech yeilds more
relevant topics.
● Proper Nouns and Verbs are represent entities and events respectively in a
document.
11. Results
● Output is a binary tree having various clusters combined at each level.
● Each non-leaf node in tree is a cluster.
● Each leaf-node is document.
● Tree is not well balanced and do suffer little from chaining if almost all
documents are of same topic.
13. Conclusion
● HTD is a newer variant over Topic detection.
● Provides multiple level of granularity.
● Major issue in the statistical approach we followed is scaling.
○ Cubic complexity of processing (document similarity matrix,
clustering)
● The relevance between the documents can be improved as we go towards
the events from documents.