This thesis studies weakly supervised learning for information extraction methods in two settings: (1) unimodal weakly supervised learning, where annotated texts are augmented with a large corpus of unlabeled texts and (2) multimodal weakly supervised learning, where images or videos are augmented with texts that describe the content of these images or videos.
In the <b>unimodal</b> setting we find that traditional semi-supervised methods based on generative Bayesian models are not suitable for the textual domain because of the violation of the assumptions made by these models. We develop an unsupervised model, the latent words language model (LWLM), that learns accurate word similarities from a large corpus of unlabeled texts. We show that this model is a good model of natural language, offering better predictive quality of unseen texts than previously proposed state-of-the-art language models. In addition, the learned word similarities can be used successfully to automatically expand words in the annotated training with synonyms, where the correct synonyms are chosen depending on the context. We show that this approach improves classifiers for word sense disambiguation and semantic role labeling.
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The second part of this thesis discusses weakly supervised learning in a <b>multimodal</b> setting. We develop information extraction methods to information from texts that describe an image or video, and use this extracted information as a weak annotation of the image/video. A first model for the prediction of entities in an image uses two novel measures: The salience measure captures the importance of an entity, depending on the position of that entity in the discourse and in the sentence. The visualness measure captures the probability that an entity can be perceived visually, extracted from the WordNet database. We show that combining these measures results in an accurate prediction of the entities present in the image. We then discuss how this model can be used to learn a mapping from names in the text to faces in the image, and to retrieve images of a certain entity.
We then turn to the automatic annotation of video. We develop a model that annotates a video with the visual verbs and their visual arguments, i.e. actions and arguments that can be observed in the video. The annotations of this system are successfully used to train a classifier that detects and classifies actions in the video. A second system annotates every scene in the video with the location of that scene. This system comprises a multimodal scene cut classifier that combines information from the text and the video, an IE algorithm that extracts possible locations from the text and a novel way to propagate location labels from one scene to another, depending the similarity of the scenes in the textual and visual domain.
This document is a thesis submitted by Shruti Ranjan Satapathy for the degree of B.Tech - M.Tech at the Indian Institute of Technology Kanpur in June 2013. It examines word sense disambiguation through both supervised and knowledge-based approaches. The supervised approach uses support vector machines and syntactic, syntacto-semantic and semantic features for all-words sense disambiguation. The knowledge-based approaches construct graphs based on WordNet and use PageRank to score word senses, showing that the approach using subgraph projections from WordNet outperforms the pairwise similarity-based approach. The thesis highlights issues with sense granularity, lack of sense-annotated training data and knowledge acquisition bottlenecks that still challenge word sense
Review: Semi-Supervised Learning Methods for Word Sense DisambiguationIOSR Journals
This document provides a review of semi-supervised learning methods for word sense disambiguation. It discusses how semi-supervised learning uses both labeled and unlabeled data, requiring only a small amount of labeled data. The document outlines several semi-supervised learning techniques for word sense disambiguation, including bootstrapping algorithms like Yarowsky's algorithm, and graph-based approaches like label propagation. It provides details on Yarowsky's bootstrapping algorithm and how it is able to generalize to label new examples through exploiting properties like one-sense-per-collocation and language redundancy.
This document discusses using a data-mining approach to perform word sense detection and disambiguation in biblical texts. It aims to identify the different senses of words in the Bible and disambiguate which sense each instance refers to. The approach uses multiple Bible translations linked to the original texts and groups instances based on translation word similarities through a progressive merging technique. This allows automatic identification of word senses using translation data in an efficient and objective manner to build sense dictionaries and enable refined Bible search and translation tools.
The Presentation contains about Word Sense Diassambiguation. I had tried to explain about the Word Sense in terms of Python language. But it can be also done using nltk.
The document outlines the draft programme for an ITS Workshop in Bordeaux, France in 2015. The workshop will discuss how intelligent transport systems (ITS) can support sustainable mobility through keynote speeches, panels, and presentations on topics like ITS expectations for the future, using ITS to reduce emissions and promote eco-driving, new mobility services, and innovations in software and data sharing. The half-day event includes sessions on ITS for sustainable transport, new services in mobility, and conclusions. Speakers will represent organizations like the UN, European Commission, French government, universities, and private companies.
An Improved Approach to Word Sense DisambiguationSurabhi Verma
This document presents a knowledge-based algorithm for word sense disambiguation that uses WordNet. It computes the similarity between a target word and nearby words based on their intersection in WordNet hierarchies, distance between the words, and hierarchical level. The algorithm was evaluated on the SemCor corpus and performed better than existing supervised and unsupervised methods by frequently ranking the correct sense first or within the top three results.
A word sense disambiguation technique for sinhalaVijayindu Gamage
This document presents a word sense disambiguation technique for Sinhala using the Sinhala WordNet. It discusses the problem of word sense disambiguation, existing solutions for other languages, and prior attempts for Sinhala. The proposed approach uses Lesk and Simplified Lesk algorithms based on comparing word glosses in the WordNet. It describes the Sinhala WordNet, system architecture, evaluation methodology and future work areas like expanding to other parts of speech and including morphology. The system achieved a precision of 0.63 and F1-score of 0.63 in initial evaluations.
Words can have more than one distinct meaning and many words can be interpreted in multiple ways
depending on the context in which they occur. The process of automatically identifying the meaning of
a polysemous word in a sentence is a fundamental task in Natural Language Processing (NLP). This
phenomenon poses challenges to Natural Language Processing systems. There have been many efforts
on word sense disambiguation for English; however, the amount of efforts for Amharic is very little.
Many natural language processing applications, such as Machine Translation, Information Retrieval,
Question Answering, and Information Extraction, require this task, which occurs at the semantic level.
In this thesis, a knowledge-based word sense disambiguation method that employs Amharic WordNet
is developed. Knowledge-based Amharic WSD extracts knowledge from word definitions and relations
among words and senses. The proposed system consists of preprocessing, morphological analysis and
disambiguation components besides Amharic WordNet database. Preprocessing is used to prepare the
input sentence for morphological analysis and morphological analysis is used to reduce various forms
of a word to a single root or stem word. Amharic WordNet contains words along with its different
meanings, synsets and semantic relations with in concepts. Finally, the disambiguation component is
used to identify the ambiguous words and assign the appropriate sense of ambiguous words in a
sentence using Amharic WordNet by using sense overlap and related words.
We have evaluated the knowledge-based Amharic word sense disambiguation using Amharic
WordNet system by conducting two experiments. The first one is evaluating the effect of Amharic
WordNet with and without morphological analyzer and the second one is determining an optimal
windows size for Amharic WSD. For Amharic WordNet with morphological analyzer and Amharic
WordNet without morphological analyzer we have achieved an accuracy of 57.5% and 80%,
respectively. In the second experiment, we have found that two-word window on each side of the
ambiguous word is enough for Amharic WSD. The test results have shown that the proposed WSD
methods have performed better than previous Amharic WSD methods.
Keywords: Natural Language Processing, Amharic WordNet, Word Sense Disambiguation,
Knowledge Based Approach, Lesk Algorithm
This document is a thesis submitted by Shruti Ranjan Satapathy for the degree of B.Tech - M.Tech at the Indian Institute of Technology Kanpur in June 2013. It examines word sense disambiguation through both supervised and knowledge-based approaches. The supervised approach uses support vector machines and syntactic, syntacto-semantic and semantic features for all-words sense disambiguation. The knowledge-based approaches construct graphs based on WordNet and use PageRank to score word senses, showing that the approach using subgraph projections from WordNet outperforms the pairwise similarity-based approach. The thesis highlights issues with sense granularity, lack of sense-annotated training data and knowledge acquisition bottlenecks that still challenge word sense
Review: Semi-Supervised Learning Methods for Word Sense DisambiguationIOSR Journals
This document provides a review of semi-supervised learning methods for word sense disambiguation. It discusses how semi-supervised learning uses both labeled and unlabeled data, requiring only a small amount of labeled data. The document outlines several semi-supervised learning techniques for word sense disambiguation, including bootstrapping algorithms like Yarowsky's algorithm, and graph-based approaches like label propagation. It provides details on Yarowsky's bootstrapping algorithm and how it is able to generalize to label new examples through exploiting properties like one-sense-per-collocation and language redundancy.
This document discusses using a data-mining approach to perform word sense detection and disambiguation in biblical texts. It aims to identify the different senses of words in the Bible and disambiguate which sense each instance refers to. The approach uses multiple Bible translations linked to the original texts and groups instances based on translation word similarities through a progressive merging technique. This allows automatic identification of word senses using translation data in an efficient and objective manner to build sense dictionaries and enable refined Bible search and translation tools.
The Presentation contains about Word Sense Diassambiguation. I had tried to explain about the Word Sense in terms of Python language. But it can be also done using nltk.
The document outlines the draft programme for an ITS Workshop in Bordeaux, France in 2015. The workshop will discuss how intelligent transport systems (ITS) can support sustainable mobility through keynote speeches, panels, and presentations on topics like ITS expectations for the future, using ITS to reduce emissions and promote eco-driving, new mobility services, and innovations in software and data sharing. The half-day event includes sessions on ITS for sustainable transport, new services in mobility, and conclusions. Speakers will represent organizations like the UN, European Commission, French government, universities, and private companies.
An Improved Approach to Word Sense DisambiguationSurabhi Verma
This document presents a knowledge-based algorithm for word sense disambiguation that uses WordNet. It computes the similarity between a target word and nearby words based on their intersection in WordNet hierarchies, distance between the words, and hierarchical level. The algorithm was evaluated on the SemCor corpus and performed better than existing supervised and unsupervised methods by frequently ranking the correct sense first or within the top three results.
A word sense disambiguation technique for sinhalaVijayindu Gamage
This document presents a word sense disambiguation technique for Sinhala using the Sinhala WordNet. It discusses the problem of word sense disambiguation, existing solutions for other languages, and prior attempts for Sinhala. The proposed approach uses Lesk and Simplified Lesk algorithms based on comparing word glosses in the WordNet. It describes the Sinhala WordNet, system architecture, evaluation methodology and future work areas like expanding to other parts of speech and including morphology. The system achieved a precision of 0.63 and F1-score of 0.63 in initial evaluations.
Words can have more than one distinct meaning and many words can be interpreted in multiple ways
depending on the context in which they occur. The process of automatically identifying the meaning of
a polysemous word in a sentence is a fundamental task in Natural Language Processing (NLP). This
phenomenon poses challenges to Natural Language Processing systems. There have been many efforts
on word sense disambiguation for English; however, the amount of efforts for Amharic is very little.
Many natural language processing applications, such as Machine Translation, Information Retrieval,
Question Answering, and Information Extraction, require this task, which occurs at the semantic level.
In this thesis, a knowledge-based word sense disambiguation method that employs Amharic WordNet
is developed. Knowledge-based Amharic WSD extracts knowledge from word definitions and relations
among words and senses. The proposed system consists of preprocessing, morphological analysis and
disambiguation components besides Amharic WordNet database. Preprocessing is used to prepare the
input sentence for morphological analysis and morphological analysis is used to reduce various forms
of a word to a single root or stem word. Amharic WordNet contains words along with its different
meanings, synsets and semantic relations with in concepts. Finally, the disambiguation component is
used to identify the ambiguous words and assign the appropriate sense of ambiguous words in a
sentence using Amharic WordNet by using sense overlap and related words.
We have evaluated the knowledge-based Amharic word sense disambiguation using Amharic
WordNet system by conducting two experiments. The first one is evaluating the effect of Amharic
WordNet with and without morphological analyzer and the second one is determining an optimal
windows size for Amharic WSD. For Amharic WordNet with morphological analyzer and Amharic
WordNet without morphological analyzer we have achieved an accuracy of 57.5% and 80%,
respectively. In the second experiment, we have found that two-word window on each side of the
ambiguous word is enough for Amharic WSD. The test results have shown that the proposed WSD
methods have performed better than previous Amharic WSD methods.
Keywords: Natural Language Processing, Amharic WordNet, Word Sense Disambiguation,
Knowledge Based Approach, Lesk Algorithm
Prediction APIs are democratizing Machine Learning. They make it easier for developers to build smart features in their apps by abstracting away some of the complexities of building and deploying predictive models. In this talk we’ll look at the possibilities and limitations of ML, how to use Prediction APIs, how to prepare data to send to them, and how to assess performance.
Similarity based methods for word sense disambiguationvini89
The document discusses methods for estimating the probability of unseen word pairs by using information from similar words. It compares four similarity-based estimation methods: KL divergence, total divergence to average, L1 norm, and confusion probability. These are evaluated against Katz's back-off scheme and maximum likelihood estimation (MLE). The total divergence to average method is found to perform the best, estimating probabilities of unseen word pairs up to 40% better than back-off and MLE methods. It works by measuring the similarity between words based on their distributions and combining evidence from similar words, weighted by their similarity.
CLIN-2015 Presentation
Word Sense Disambiguation is still an unsolved problem in Natural Language Processing.
We claim that most approaches do not model the context correctly, by relying
too much on the local context (the words surrounding the word in question), or on
the most frequent sense of a word. In order to provide evidence for this claim, we
conducted an in-depth analysis of all-words tasks of the competitions that have been
organized (Senseval 2&3, Semeval-2007, Semeval-2010, Semeval 2013). We focused
on the average error rate per competition and across competitions per part of speech,
lemma, relative frequency class, and polysemy class. In addition, we inspected the
“difficulty” of a token(word) by calculating the average polysemy of the words in the
sentence of a token. Finally, we inspected to what extent systems always chose the
most frequent sense. The results from Senseval 2, which are representative of other
competitions, showed that the average error rate for monosemous words was 33.3%
due to part of speech errors. This number was 71% for multiword and phrasal verbs.
In addition, we observe that higher polysemy yields a higher error rate. Moreover, we
do not observe a drop in the error rate if there are multiple occurrences of the same
lemma, which might indicate that systems rely mostly on the sentence itself. Finally,
out of the 799 tokens for which the correct sense was not the most frequent sense, system
still assigned the most frequent sense in 84% of the cases. For future work, we plan
to develop a strategy in order to determine in which context the predominant sense
should be assigned, and more importantly when it should not be assigned. One of the
most important parts of this strategy would be to not only determine the meaning of
a specific word, but to also know it’s referential meaning. For example, in the case of
the lemma ‘winner’, we do not only want to know what ‘winner’ means, but we also
want to know what this ‘winner’ won and who this ‘winner’ was.
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.
The document discusses word sense disambiguation and induction. It introduces the general problem of ambiguity in language and different word sense disambiguation tasks. It covers approaches to representing context, knowledge resources used, applications of WSD, and supervised and knowledge-based WSD methods including gloss overlap, lexical chains, and PageRank.
Slides from a short presentation given on the features of two plagiarism detection tools: Turnitin and SafeAssign at a recent Faculty Forum on Teaching and Technology (1/26/12)
for more about the Faculty Forum:
http://www.fordham.edu/campus_resources/fordham_it/help__support/faculty__staff/faculty_technology_s/faculty_forum_on_tea_78160.asp
This document summarizes a lecture on word sense disambiguation and shallow semantics. It discusses word sense ambiguity and common approaches to word sense disambiguation, including knowledge-based methods like Lesk's algorithm and supervised machine learning methods. It also covers semantic analysis techniques like augmenting syntactic rules with semantic attachments and representing verb semantics using frames and semantic roles. Finally, it provides an overview of the PropBank corpus for semantic role labeling.
Design science, systems thinking and ontologies summary-upward a-v1.0Antony Upward
The document discusses designing an ontology for strongly sustainable business models using a systems approach and design science methodology. It proposes:
1) Setting clear objectives to minimize bias and gain feedback for improvement.
2) Iteratively building the ontology by examining its fundamental building blocks - function, structure, process, and context - through inquiry.
3) Evaluating the ontology using a diversity of knowledge sources to triangulate different worldviews for a well-rounded assessment.
plagiarism detection tools and techniquesNimisha T
The document discusses various techniques for detecting plagiarism in text and source code. It defines plagiarism and describes how to avoid it through prevention and detection. For text, it covers substring matching, keyword similarity, fingerprint matching, and text parsing techniques. For source code, it discusses lexical similarities, parse trees, program dependence graphs, and metrics. It also provides examples of tools used for each type of plagiarism detection like PlagAware, MOSS, and JPlag.
The document discusses plagiarism and its negative consequences. It defines plagiarism as copying another's writing and submitting it as one's own work. Several examples are provided of students committing plagiarism by copying articles from sources and passing them off as their own work to a teacher. The document emphasizes that plagiarism is considered cheating, is not permitted in any class or university, and can result in failure if detected. It also notes that while the internet makes plagiarism easier, it also enables teachers to more easily identify plagiarized content. Students are advised to only submit their original work and to ask the teacher if they have any questions about plagiarism.
Technical analysis is the study of price, volume, and open interest to forecast market trends by analyzing charts and indicators. It is based on the assumptions that current prices reflect all known information, prices trend over time, and history repeats. Technical analysis focuses on market effects rather than fundamental causes and can be applied quickly to any market. It helps understand market psychology and short-term movements.
The document discusses the use of neural networks and deep learning techniques like word2vec and seq2seq models to develop representations of language that computers can understand without explicit symbolic representations or rules. It notes that while these techniques have achieved success, computers still lack a grounded understanding of language and the ability to reason about language based on real-world experiences and commonsense knowledge.
From Natural Language Processing to Artificial IntelligenceJonathan Mugan
Overview of natural language processing (NLP) from both symbolic and deep learning perspectives. Covers tf-idf, sentiment analysis, LDA, WordNet, FrameNet, word2vec, and recurrent neural networks (RNNs).
The document summarizes research on using different semantic techniques like contexts, co-occurrences, and ontologies to build a "semantic quilt" that can be used for natural language processing tasks. It discusses using n-gram statistics to identify associated words, sense clusters to identify similar contexts, and WordNet to measure conceptual similarity. The goal is to integrate these different semantic resources and methods to solve problems with less reliance on manually built resources.
When you look at content strategy closely, you'll discover it runs through virtually every discipline—both online and off—from web development to service design to advertising. Once you understand the generic principles, you can apply content strategy anywhere you choose.
This document provides an overview of a tutorial on word sense disambiguation (WSD). The tutorial aims to introduce the problem of WSD and various approaches, including knowledge-intensive methods, supervised learning approaches, and unsupervised learning. It covers the history of WSD, theoretical connections to other fields, practical applications, and an outline of the different parts of the tutorial.
This document discusses cognitive psychology and content design principles. It covers 6 main topics: 1) attention and how to manage it through design, 2) vision and how people process visual information, 3) working memory limitations and how to reduce cognitive load, 4) using plain language and reducing complex words, 5) the effects of stress on cognition, and 6) answering questions about design and cognition. The overall message is that content design should account for cognitive limitations by reducing extraneous cognitive load and designing for limited working memory.
The document summarizes a tutorial on word sense disambiguation (WSD) given at AAAI-2005. It introduces the problem of WSD, outlines different approaches including knowledge-intensive methods, supervised learning, minimally supervised and unsupervised learning. The tutorial aims to introduce WSD and persuade the audience to work on and apply WSD in their text applications.
The document summarizes a tutorial on word sense disambiguation (WSD) given at AAAI-2005. It introduces the problem of WSD, outlines different approaches including knowledge-intensive methods, supervised learning, minimally supervised and unsupervised learning. The tutorial aims to introduce WSD and persuade the audience to work on and apply WSD in their text applications.
How can text-mining leverage developments in Deep Learning? Presentation at ...jcscholtes
How can text-mining leverage developments in Deep Learning?
Text-mining focusses primary on extracting complex patterns from unstructured electronic data sets and applying machine learning for document classification. During the last decade, a generation of efficient and successful algorithms has been developed using bag-of-words models to represent document content and statistical and geometrical machine learning algorithms such as Conditional Random Fields and Support Vector Machines. These algorithms require relatively little training data and are fast on modern hardware. However, performance seems to be stuck around 90% F1 values.
In computer vision, deep learning has shown great success where the 90% barrier has been broken in many application. In addition, deep learning also shows new successes for transfer learning and self-learning such as reinforcement leaning. Dedicated hardware helped us to overcome computational challenges and methods such as training data augmentation solved the need for unrealistically large data sets.
So, it would make sense to apply deep learning also on textual data as well. But how do we represent textual data: there are many different methods for word embeddings and as many deep learning architectures. Training data augmentation, transfer learning and reinforcement leaning are not fully defined for textual data.
Prediction APIs are democratizing Machine Learning. They make it easier for developers to build smart features in their apps by abstracting away some of the complexities of building and deploying predictive models. In this talk we’ll look at the possibilities and limitations of ML, how to use Prediction APIs, how to prepare data to send to them, and how to assess performance.
Similarity based methods for word sense disambiguationvini89
The document discusses methods for estimating the probability of unseen word pairs by using information from similar words. It compares four similarity-based estimation methods: KL divergence, total divergence to average, L1 norm, and confusion probability. These are evaluated against Katz's back-off scheme and maximum likelihood estimation (MLE). The total divergence to average method is found to perform the best, estimating probabilities of unseen word pairs up to 40% better than back-off and MLE methods. It works by measuring the similarity between words based on their distributions and combining evidence from similar words, weighted by their similarity.
CLIN-2015 Presentation
Word Sense Disambiguation is still an unsolved problem in Natural Language Processing.
We claim that most approaches do not model the context correctly, by relying
too much on the local context (the words surrounding the word in question), or on
the most frequent sense of a word. In order to provide evidence for this claim, we
conducted an in-depth analysis of all-words tasks of the competitions that have been
organized (Senseval 2&3, Semeval-2007, Semeval-2010, Semeval 2013). We focused
on the average error rate per competition and across competitions per part of speech,
lemma, relative frequency class, and polysemy class. In addition, we inspected the
“difficulty” of a token(word) by calculating the average polysemy of the words in the
sentence of a token. Finally, we inspected to what extent systems always chose the
most frequent sense. The results from Senseval 2, which are representative of other
competitions, showed that the average error rate for monosemous words was 33.3%
due to part of speech errors. This number was 71% for multiword and phrasal verbs.
In addition, we observe that higher polysemy yields a higher error rate. Moreover, we
do not observe a drop in the error rate if there are multiple occurrences of the same
lemma, which might indicate that systems rely mostly on the sentence itself. Finally,
out of the 799 tokens for which the correct sense was not the most frequent sense, system
still assigned the most frequent sense in 84% of the cases. For future work, we plan
to develop a strategy in order to determine in which context the predominant sense
should be assigned, and more importantly when it should not be assigned. One of the
most important parts of this strategy would be to not only determine the meaning of
a specific word, but to also know it’s referential meaning. For example, in the case of
the lemma ‘winner’, we do not only want to know what ‘winner’ means, but we also
want to know what this ‘winner’ won and who this ‘winner’ was.
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.
The document discusses word sense disambiguation and induction. It introduces the general problem of ambiguity in language and different word sense disambiguation tasks. It covers approaches to representing context, knowledge resources used, applications of WSD, and supervised and knowledge-based WSD methods including gloss overlap, lexical chains, and PageRank.
Slides from a short presentation given on the features of two plagiarism detection tools: Turnitin and SafeAssign at a recent Faculty Forum on Teaching and Technology (1/26/12)
for more about the Faculty Forum:
http://www.fordham.edu/campus_resources/fordham_it/help__support/faculty__staff/faculty_technology_s/faculty_forum_on_tea_78160.asp
This document summarizes a lecture on word sense disambiguation and shallow semantics. It discusses word sense ambiguity and common approaches to word sense disambiguation, including knowledge-based methods like Lesk's algorithm and supervised machine learning methods. It also covers semantic analysis techniques like augmenting syntactic rules with semantic attachments and representing verb semantics using frames and semantic roles. Finally, it provides an overview of the PropBank corpus for semantic role labeling.
Design science, systems thinking and ontologies summary-upward a-v1.0Antony Upward
The document discusses designing an ontology for strongly sustainable business models using a systems approach and design science methodology. It proposes:
1) Setting clear objectives to minimize bias and gain feedback for improvement.
2) Iteratively building the ontology by examining its fundamental building blocks - function, structure, process, and context - through inquiry.
3) Evaluating the ontology using a diversity of knowledge sources to triangulate different worldviews for a well-rounded assessment.
plagiarism detection tools and techniquesNimisha T
The document discusses various techniques for detecting plagiarism in text and source code. It defines plagiarism and describes how to avoid it through prevention and detection. For text, it covers substring matching, keyword similarity, fingerprint matching, and text parsing techniques. For source code, it discusses lexical similarities, parse trees, program dependence graphs, and metrics. It also provides examples of tools used for each type of plagiarism detection like PlagAware, MOSS, and JPlag.
The document discusses plagiarism and its negative consequences. It defines plagiarism as copying another's writing and submitting it as one's own work. Several examples are provided of students committing plagiarism by copying articles from sources and passing them off as their own work to a teacher. The document emphasizes that plagiarism is considered cheating, is not permitted in any class or university, and can result in failure if detected. It also notes that while the internet makes plagiarism easier, it also enables teachers to more easily identify plagiarized content. Students are advised to only submit their original work and to ask the teacher if they have any questions about plagiarism.
Technical analysis is the study of price, volume, and open interest to forecast market trends by analyzing charts and indicators. It is based on the assumptions that current prices reflect all known information, prices trend over time, and history repeats. Technical analysis focuses on market effects rather than fundamental causes and can be applied quickly to any market. It helps understand market psychology and short-term movements.
The document discusses the use of neural networks and deep learning techniques like word2vec and seq2seq models to develop representations of language that computers can understand without explicit symbolic representations or rules. It notes that while these techniques have achieved success, computers still lack a grounded understanding of language and the ability to reason about language based on real-world experiences and commonsense knowledge.
From Natural Language Processing to Artificial IntelligenceJonathan Mugan
Overview of natural language processing (NLP) from both symbolic and deep learning perspectives. Covers tf-idf, sentiment analysis, LDA, WordNet, FrameNet, word2vec, and recurrent neural networks (RNNs).
The document summarizes research on using different semantic techniques like contexts, co-occurrences, and ontologies to build a "semantic quilt" that can be used for natural language processing tasks. It discusses using n-gram statistics to identify associated words, sense clusters to identify similar contexts, and WordNet to measure conceptual similarity. The goal is to integrate these different semantic resources and methods to solve problems with less reliance on manually built resources.
When you look at content strategy closely, you'll discover it runs through virtually every discipline—both online and off—from web development to service design to advertising. Once you understand the generic principles, you can apply content strategy anywhere you choose.
This document provides an overview of a tutorial on word sense disambiguation (WSD). The tutorial aims to introduce the problem of WSD and various approaches, including knowledge-intensive methods, supervised learning approaches, and unsupervised learning. It covers the history of WSD, theoretical connections to other fields, practical applications, and an outline of the different parts of the tutorial.
This document discusses cognitive psychology and content design principles. It covers 6 main topics: 1) attention and how to manage it through design, 2) vision and how people process visual information, 3) working memory limitations and how to reduce cognitive load, 4) using plain language and reducing complex words, 5) the effects of stress on cognition, and 6) answering questions about design and cognition. The overall message is that content design should account for cognitive limitations by reducing extraneous cognitive load and designing for limited working memory.
The document summarizes a tutorial on word sense disambiguation (WSD) given at AAAI-2005. It introduces the problem of WSD, outlines different approaches including knowledge-intensive methods, supervised learning, minimally supervised and unsupervised learning. The tutorial aims to introduce WSD and persuade the audience to work on and apply WSD in their text applications.
The document summarizes a tutorial on word sense disambiguation (WSD) given at AAAI-2005. It introduces the problem of WSD, outlines different approaches including knowledge-intensive methods, supervised learning, minimally supervised and unsupervised learning. The tutorial aims to introduce WSD and persuade the audience to work on and apply WSD in their text applications.
How can text-mining leverage developments in Deep Learning? Presentation at ...jcscholtes
How can text-mining leverage developments in Deep Learning?
Text-mining focusses primary on extracting complex patterns from unstructured electronic data sets and applying machine learning for document classification. During the last decade, a generation of efficient and successful algorithms has been developed using bag-of-words models to represent document content and statistical and geometrical machine learning algorithms such as Conditional Random Fields and Support Vector Machines. These algorithms require relatively little training data and are fast on modern hardware. However, performance seems to be stuck around 90% F1 values.
In computer vision, deep learning has shown great success where the 90% barrier has been broken in many application. In addition, deep learning also shows new successes for transfer learning and self-learning such as reinforcement leaning. Dedicated hardware helped us to overcome computational challenges and methods such as training data augmentation solved the need for unrealistically large data sets.
So, it would make sense to apply deep learning also on textual data as well. But how do we represent textual data: there are many different methods for word embeddings and as many deep learning architectures. Training data augmentation, transfer learning and reinforcement leaning are not fully defined for textual data.
Smart Data Webinar: Advances in Natural Language Processing I - UnderstandingDATAVERSITY
Natural Language Processing (NLP) – once on the frontier of AI as a research topic with maddeningly low accuracy – is rapidly becoming a requirement for mainstream consumer, enterprise, and public sector applications. Today, one can build a system that allows natural language text or speech input without knowing much more than a few API specs. From chatbots to search engine translation services to applications that scour social media posts looking for business opportunities or terror threats, Natural Language Understanding (NLU) is delivering value today.
In this webinar, we will cover the basics of NLU processing and knowledge representation, semantic analysis for text analytics, and recent advances in translation functionality driven by machine learning. Participants will learn how modern approaches have gone beyond counting words with statistical models to predicting speech the way people fill in sentences with context while listening. We will also present examples of commercially available NLP APIs to help participants experiment with NLP in their own applications right away.
Aldo Gangemi - Meaning on the Web: An Empirical Design Perspectivesssw2012
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Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/TBJqgvXYhfo.
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Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
Sameer's Bio:
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
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1. In the first conversation, the Quality maxim is being flouted.
2. In the second conversation, the Quantity maxim is being flouted.
3. In the third conversation, the Relevance maxim is being flouted.
4. In the fourth conversation, the Manner maxim of avoiding ambiguity is being flouted.
2. Yes, the implicatures are successful in each case because the hearer recognizes that a maxim is being flouted and is able to infer the implicated meaning.
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The document discusses different approaches to generating biographies through natural language processing, including information extraction and language modeling. It describes using information extraction patterns learned from Wikipedia to extract fields like date of birth and place of birth, and bouncing between Wikipedia and Google search results to learn patterns for other fields with less structured data. It also proposes selecting and ranking sentences from search results to improve recall when information extraction may miss relevant sentences. The goal is to build biographies by combining these techniques for high precision on structured fields and better recall on more complex fields.
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We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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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.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
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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.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
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The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
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11. Example: WSD
Soft rules :
If “kicked”
If “goal” ball = “round object”
...
If “dance”
If “gown” ball = “formal dance”
...
Machine learning methods can combine many
complimentary and/or contradicting rules
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12. Supervised machine learning
Current stateoftheart machine learning
methods
Manually annotated corpus
Machine learning method
needed for every new task,
often independent of task
language or domain
Successful for many tasks
Features need to be
Flexible, fast development
manually engineered
for new tasks
High variation of language
Only some expert
limits performance even
knowledge needed
with large training corpora
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13. Solution: use unlabeled data
Unlabeled data: cheap, available for many
domains and languages
Semisupervised learning
Optimize single function that incorporates labeled
and unlabeled data
Violation of assumptions cause deteriorating results
when adding more unlabeled data
Unsupervised learning
First learn model on unlabeled data, then use model
in supervised machine learning method
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18. Latent words language model
We hope there is an increasing need for reform
We hope there is an increasing need for reform
I believe this was the enormous chance of restructuring
They think that 's no important demand to change
You feel it are some increased potential that peace
... ... ... ... ... ... ... ... ...
Automatically learned synonyms
18
19. Latent words language model
We hope there is an increasing need for reform
We hope there is an increasing need for reform
I believe this was the enormous chance of restructuring
They think that 's no important demand to change
You feel it are some increased potential that peace
... ... ... ... ... ... ... ... ...
Time to compute all possible combinations:
~ very, very long...
Approximate: consider only most likely
~ pretty fast
19
21. LWLM for information extraction
Word sense disambiguation
standard + cluster features + hidden words
66.32% 66.97% 67.61%
Semantic role labeling
90%
80%
70%
60%
standard
50% + clusters
40% + hidden words
5% 20% 50% 100%
Latent words : help with underspecification and
ambiguity
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24. Annotation of entities in images
Extract entities from descriptive news text that
are present in the image.
Former President Bill Clinton, left, looks on as an honor guard
folds the U.S. flag during a graveside service for Lloyd Bentsen
in Houston, May 30, 2006. Bentsen, a former senator and
former treasury secretary, died last week at the age of 85.
service
Lloyd Bentsen
Bill Clinton
Houston
guard age
flag ...
24
25. Annotation of entities in images
Assumption:
Entity is present in image if important in
descriptive text and possible to perceive visually.
Salience:
Dependent on text
Combines analysis of discourse and syntax
Visualness:
Independent of text
Extracted from semantic database
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27. Salience
Is the entity important in descriptive text?
Discourse model
Important entities are referred to by other entities
and terms.
Graph models entities, coreferents and other terms
Eigenvectors find most important entities
Syntactic model
Important entities appear high in parse tree
Important entities have many children in tree
27
28. Visualness
Can the entity be perceived visually?
Similarity measure on entities in WordNet
s(“car”,“truck”) = 0.88 s(“thought”,“house”) = 0.23
s(“car”,“horse”) = 0.38 s(“house”,“building”) = 0.91
s(“horse”, “cow”) = 0.79 s(“car”, “house”) = 0.40
Visual seeds “person”, “vehicle” , “animal”, ...
Nonvisual seeds “thought”, “power”, “air”, …
Visualness:
combine similarity measure and seeds
“entities close to visual seeds will be visual”
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32. Scene segmentation
Segment transcript and video in scenes
Scene cut classifier in text
Shot cut detector in video
Transcript
Shot of Buffy opening the refrigerator and taking out a carton of milk.
Scene cuts
Buffy sniffs the milk and puts it on the counter. In the background we
see Joyce drinking coffee and Dawn opening a cabinet to get out a box
of cereal. ...
Buffy & Riley move into the living room. They sit on the sofa.
Buffy nods in resignation. Smooch. Riley gets up.
Cut to a shot of a bright red convertible driving down the street. Giles
is at the wheel, Buffy beside him and Dawn in the back. Classical
music plays on the radio.
....
32
34. Scene segmentation
Segment transcript and video in scenes
Scene cut classifier in text
Shot cut detector in video
Shot of Buffy opening the
refrigerator and taking out a
carton of milk.
...
Buffy & Riley move into the
living room. They sit on the
sofa.
…
Cut to a shot of a bright red
convertible driving down the
street.
....
34
36. Location annotation results
Scene cut classifier precision recall f1measure
91.71% 97.48% 85.16%
Location detector precision recall f1measure
68.75% 75.54% 71.98%
Location annotation
episode only text text + LDA text + LDA + vision
2 54.72% 58.89% 57.39%
3 60.11% 65.87% 68.57%
36
37. Contributions 1/2
The latent words language model
Best ngram language model
Unsupervised learning of word similarities
Unsupervised disambiguation of words
Using the latent words for WSD
Best WSD system
Using the latent words for SRL
Improvement of soa classifier
37
38. Contributions 2/2
Image annotation :
First full analysis of entities in descriptive texts
Visualness: capture knowledge from WordNet
Salience: capture knowledge from syntactic
properties
Location annotation :
Automatic annotation of locations from transcripts
Including new locations
Including locations that are not explicitly mentioned
38