The human brain is quite proficient at word-sense disambiguation. The fact that natural language is formed in a way that requires so much of it is a reflection of that neurologic reality. In other words, human language developed in a way that reflects (and also has helped to shape) the innate ability provided by the brain's neural networks. In computer science and the information technology that it enables, it has been a long-term challenge to develop the ability in computers to do natural language processing and machine learning.
To date, a rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date.
Adjusting sense representations for knowledge-based word sense disambiguation
Speaker: Tristan Miller, Technische Universität Darmstadt (Germany)
Abstract: Word sense disambiguation (WSD) – the task of determining which meaning a word carries in a particular context – is a core research problem in computational linguistics. Though it has long been recognized that supervised (i.e., machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and text domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for deliberate effect in humour and wordplay. In contrast to supervised systems are knowledge-based techniques, whi...
published: 31 May 2017
Word Sense Disambiguation 🔥
This video tutorial is about Word Sense Disambiguation in Natural Language Processing ( nlp ) in the language Hindi using lesk algorithm.
Purchase notes right now,
more details below:
https://perfectcomputerengineer.classx.co.in/new-courses/13-natural-language-processing-notes
* Natural Language Processing Playlist:
https://youtube.com/playlist?list=PLPIwNooIb9vimsumdWeKF3BRzs9tJ-_gy
* Human-Machine Interaction entire Playlist:
https://www.youtube.com/playlist?list=PLPIwNooIb9vhFRT_3JDQ0CGbW5HeFg3yK
* Distributed Computing:
https://youtube.com/playlist?list=PLPIwNooIb9vhYroMrNpoBYiBUFzTwEZot
*Gears used for this YouTube Channel:
https://linktr.ee/perfectcomputerengineer
*Let's connect:
Instagram: https://www.instagram.com/planetojas/
published: 05 Dec 2021
CS224u - Distributed word representations: word-sense disambiguation
published: 20 Mar 2015
Word Sense Disambiguation
Material based on Jurafsky and Martin (2019): https://web.stanford.edu/~jurafsky/slp3/
Slides: http://www.natalieparde.com/teaching/cs_421_fall2020/Word%20Sense%20Disambiguation.pdf
Twitter: @NatalieParde
published: 28 Dec 2020
Part - 12 Word Sense Disambiguation in NLTK with python (Natural Language Toolkit Tutorial)
https://github.com/dsarchives/NLTK
Hello everyone, welcome back!
In this video, we'll learn about WSD(Word Sense Disambiguation)
In computational linguistics, word-sense disambiguation is an open problem concerned with identifying which sense of a word is used in a sentence.
NLTK use lesk algorithm for that
In natural language processing, word sense disambiguation (WSD) is the problem of determining which "sense" (meaning) of a word is activated by the use of the word in a particular context
Given an ambiguous word and the context in which the word occurs, Lesk returns a Synset with the highest number of overlapping words between the context sentence and different definitions from each Synset.
#wsd #nltk #tutorials
published: 27 Sep 2020
Word Sense Disambiguation on neweraHPC Distributed System
Word Sense Disambiguation using Princeton WordNet dictionary.
It works well on the High Performance Computing framework, neweraHPC.
Project Website http://newerahpc.com
published: 13 Mar 2013
🖋 Word Sense Disambiguation in Colab using KerasNLP.
A knowledge based approach to word sense disambiguation using Wordnet
published: 28 May 2014
AI Seminar: Yixing Luan, Leveraging Translations for Word Sense Disambiguation (July 31)
Amii researcher (under the supervision of Amii Fellow Greg Kondrak) Yixing Luan presents "Leveraging Translations for Word Sense Disambiguation" at the AI Seminar (July 31, 2020).
The Artificial Intelligence (AI) Seminar is a weekly meeting at the University of Alberta where researchers interested in AI can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems, are explored.
Bio: Yixing Luan is a thesis-based M.Sc. candidate in Computing Science at the University of Alberta, supervised by Dr. Greg Kondrak. His research interest includes artificial intellig...
Speaker: Tristan Miller, Technische Universität Darmstadt (Germany)
Abstract: Word sense disambiguation (WSD) – the task of determining which meaning a word ca...
Speaker: Tristan Miller, Technische Universität Darmstadt (Germany)
Abstract: Word sense disambiguation (WSD) – the task of determining which meaning a word carries in a particular context – is a core research problem in computational linguistics. Though it has long been recognized that supervised (i.e., machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and text domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for deliberate effect in humour and wordplay. In contrast to supervised systems are knowledge-based techniques, which rely only on pre-existing lexical-semantic resources (LSRs) such as dictionaries and thesauri. These techniques are of more general applicability but tend to suffer from lower performance due to the informational gap between the target word's context and the sense descriptions provided by the LSR. In this seminar, we treat the task of extending the efficacy and applicability of knowledge-based WSD, both generally and for the particular case of English puns. In the first part of the talk, we present two approaches for bridging the information gap and thereby improving WSD coverage and accuracy. In the first approach, we supplement the word's context and the LSR's sense descriptions with entries from a distributional thesaurus. The second approach enriches an LSR's sense information by aligning it to other, complementary LSRs. In the second part of the talk, we describe how these techniques, along with evaluation methodologies from traditional WSD, can be adapted for the "disambiguation" of puns, or rather for the automatic identification of their double meanings.
Speaker: Tristan Miller, Technische Universität Darmstadt (Germany)
Abstract: Word sense disambiguation (WSD) – the task of determining which meaning a word carries in a particular context – is a core research problem in computational linguistics. Though it has long been recognized that supervised (i.e., machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and text domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for deliberate effect in humour and wordplay. In contrast to supervised systems are knowledge-based techniques, which rely only on pre-existing lexical-semantic resources (LSRs) such as dictionaries and thesauri. These techniques are of more general applicability but tend to suffer from lower performance due to the informational gap between the target word's context and the sense descriptions provided by the LSR. In this seminar, we treat the task of extending the efficacy and applicability of knowledge-based WSD, both generally and for the particular case of English puns. In the first part of the talk, we present two approaches for bridging the information gap and thereby improving WSD coverage and accuracy. In the first approach, we supplement the word's context and the LSR's sense descriptions with entries from a distributional thesaurus. The second approach enriches an LSR's sense information by aligning it to other, complementary LSRs. In the second part of the talk, we describe how these techniques, along with evaluation methodologies from traditional WSD, can be adapted for the "disambiguation" of puns, or rather for the automatic identification of their double meanings.
This video tutorial is about Word Sense Disambiguation in Natural Language Processing ( nlp ) in the language Hindi using lesk algorithm.
Purchase notes right ...
This video tutorial is about Word Sense Disambiguation in Natural Language Processing ( nlp ) in the language Hindi using lesk algorithm.
Purchase notes right now,
more details below:
https://perfectcomputerengineer.classx.co.in/new-courses/13-natural-language-processing-notes
* Natural Language Processing Playlist:
https://youtube.com/playlist?list=PLPIwNooIb9vimsumdWeKF3BRzs9tJ-_gy
* Human-Machine Interaction entire Playlist:
https://www.youtube.com/playlist?list=PLPIwNooIb9vhFRT_3JDQ0CGbW5HeFg3yK
* Distributed Computing:
https://youtube.com/playlist?list=PLPIwNooIb9vhYroMrNpoBYiBUFzTwEZot
*Gears used for this YouTube Channel:
https://linktr.ee/perfectcomputerengineer
*Let's connect:
Instagram: https://www.instagram.com/planetojas/
This video tutorial is about Word Sense Disambiguation in Natural Language Processing ( nlp ) in the language Hindi using lesk algorithm.
Purchase notes right now,
more details below:
https://perfectcomputerengineer.classx.co.in/new-courses/13-natural-language-processing-notes
* Natural Language Processing Playlist:
https://youtube.com/playlist?list=PLPIwNooIb9vimsumdWeKF3BRzs9tJ-_gy
* Human-Machine Interaction entire Playlist:
https://www.youtube.com/playlist?list=PLPIwNooIb9vhFRT_3JDQ0CGbW5HeFg3yK
* Distributed Computing:
https://youtube.com/playlist?list=PLPIwNooIb9vhYroMrNpoBYiBUFzTwEZot
*Gears used for this YouTube Channel:
https://linktr.ee/perfectcomputerengineer
*Let's connect:
Instagram: https://www.instagram.com/planetojas/
Material based on Jurafsky and Martin (2019): https://web.stanford.edu/~jurafsky/slp3/
Slides: http://www.natalieparde.com/teaching/cs_421_fall2020/Word%20Sens...
Material based on Jurafsky and Martin (2019): https://web.stanford.edu/~jurafsky/slp3/
Slides: http://www.natalieparde.com/teaching/cs_421_fall2020/Word%20Sense%20Disambiguation.pdf
Twitter: @NatalieParde
Material based on Jurafsky and Martin (2019): https://web.stanford.edu/~jurafsky/slp3/
Slides: http://www.natalieparde.com/teaching/cs_421_fall2020/Word%20Sense%20Disambiguation.pdf
Twitter: @NatalieParde
https://github.com/dsarchives/NLTK
Hello everyone, welcome back!
In this video, we'll learn about WSD(Word Sense Disambiguation)
In computational linguistic...
https://github.com/dsarchives/NLTK
Hello everyone, welcome back!
In this video, we'll learn about WSD(Word Sense Disambiguation)
In computational linguistics, word-sense disambiguation is an open problem concerned with identifying which sense of a word is used in a sentence.
NLTK use lesk algorithm for that
In natural language processing, word sense disambiguation (WSD) is the problem of determining which "sense" (meaning) of a word is activated by the use of the word in a particular context
Given an ambiguous word and the context in which the word occurs, Lesk returns a Synset with the highest number of overlapping words between the context sentence and different definitions from each Synset.
#wsd #nltk #tutorials
https://github.com/dsarchives/NLTK
Hello everyone, welcome back!
In this video, we'll learn about WSD(Word Sense Disambiguation)
In computational linguistics, word-sense disambiguation is an open problem concerned with identifying which sense of a word is used in a sentence.
NLTK use lesk algorithm for that
In natural language processing, word sense disambiguation (WSD) is the problem of determining which "sense" (meaning) of a word is activated by the use of the word in a particular context
Given an ambiguous word and the context in which the word occurs, Lesk returns a Synset with the highest number of overlapping words between the context sentence and different definitions from each Synset.
#wsd #nltk #tutorials
Word Sense Disambiguation using Princeton WordNet dictionary.
It works well on the High Performance Computing framework, neweraHPC.
Project Website http://newer...
Word Sense Disambiguation using Princeton WordNet dictionary.
It works well on the High Performance Computing framework, neweraHPC.
Project Website http://newerahpc.com
Word Sense Disambiguation using Princeton WordNet dictionary.
It works well on the High Performance Computing framework, neweraHPC.
Project Website http://newerahpc.com
Amii researcher (under the supervision of Amii Fellow Greg Kondrak) Yixing Luan presents "Leveraging Translations for Word Sense Disambiguation" at the AI Semin...
Amii researcher (under the supervision of Amii Fellow Greg Kondrak) Yixing Luan presents "Leveraging Translations for Word Sense Disambiguation" at the AI Seminar (July 31, 2020).
The Artificial Intelligence (AI) Seminar is a weekly meeting at the University of Alberta where researchers interested in AI can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems, are explored.
Bio: Yixing Luan is a thesis-based M.Sc. candidate in Computing Science at the University of Alberta, supervised by Dr. Greg Kondrak. His research interest includes artificial intelligence and natural language processing with a focus on lexical semantics. He completed his bachelor's degree at Hokkaido University, Japan.
Abstract: Word sense disambiguation (WSD) is one of the core tasks in Natural Language Processing and its objective is to identify the correct sense of a content word in context. Although WSD is a monolingual task, it has been conjectured that multilingual information, e.g. translations, can be helpful. However, existing WSD systems rarely consider multilingual information, and no effective method has been proposed for improving WSD with machine translation. In this work, we propose methods of leveraging translations from multiple languages as a constraint to boost the accuracy of existing WSD systems. To this end, we also develop a novel knowledge-based word alignment algorithm, which outperforms an existing word alignment tool in our intrinsic and extrinsic evaluations. Since our approach is language-independent, we perform WSD experiments on standard benchmark datasets representing several languages. The results demonstrate that our methods can consistently improve the performance of various WSD systems, and obtain state-of-the-art results in both English and multilingual WSD.
Amii researcher (under the supervision of Amii Fellow Greg Kondrak) Yixing Luan presents "Leveraging Translations for Word Sense Disambiguation" at the AI Seminar (July 31, 2020).
The Artificial Intelligence (AI) Seminar is a weekly meeting at the University of Alberta where researchers interested in AI can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems, are explored.
Bio: Yixing Luan is a thesis-based M.Sc. candidate in Computing Science at the University of Alberta, supervised by Dr. Greg Kondrak. His research interest includes artificial intelligence and natural language processing with a focus on lexical semantics. He completed his bachelor's degree at Hokkaido University, Japan.
Abstract: Word sense disambiguation (WSD) is one of the core tasks in Natural Language Processing and its objective is to identify the correct sense of a content word in context. Although WSD is a monolingual task, it has been conjectured that multilingual information, e.g. translations, can be helpful. However, existing WSD systems rarely consider multilingual information, and no effective method has been proposed for improving WSD with machine translation. In this work, we propose methods of leveraging translations from multiple languages as a constraint to boost the accuracy of existing WSD systems. To this end, we also develop a novel knowledge-based word alignment algorithm, which outperforms an existing word alignment tool in our intrinsic and extrinsic evaluations. Since our approach is language-independent, we perform WSD experiments on standard benchmark datasets representing several languages. The results demonstrate that our methods can consistently improve the performance of various WSD systems, and obtain state-of-the-art results in both English and multilingual WSD.
Speaker: Tristan Miller, Technische Universität Darmstadt (Germany)
Abstract: Word sense disambiguation (WSD) – the task of determining which meaning a word carries in a particular context – is a core research problem in computational linguistics. Though it has long been recognized that supervised (i.e., machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and text domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for deliberate effect in humour and wordplay. In contrast to supervised systems are knowledge-based techniques, which rely only on pre-existing lexical-semantic resources (LSRs) such as dictionaries and thesauri. These techniques are of more general applicability but tend to suffer from lower performance due to the informational gap between the target word's context and the sense descriptions provided by the LSR. In this seminar, we treat the task of extending the efficacy and applicability of knowledge-based WSD, both generally and for the particular case of English puns. In the first part of the talk, we present two approaches for bridging the information gap and thereby improving WSD coverage and accuracy. In the first approach, we supplement the word's context and the LSR's sense descriptions with entries from a distributional thesaurus. The second approach enriches an LSR's sense information by aligning it to other, complementary LSRs. In the second part of the talk, we describe how these techniques, along with evaluation methodologies from traditional WSD, can be adapted for the "disambiguation" of puns, or rather for the automatic identification of their double meanings.
This video tutorial is about Word Sense Disambiguation in Natural Language Processing ( nlp ) in the language Hindi using lesk algorithm.
Purchase notes right now,
more details below:
https://perfectcomputerengineer.classx.co.in/new-courses/13-natural-language-processing-notes
* Natural Language Processing Playlist:
https://youtube.com/playlist?list=PLPIwNooIb9vimsumdWeKF3BRzs9tJ-_gy
* Human-Machine Interaction entire Playlist:
https://www.youtube.com/playlist?list=PLPIwNooIb9vhFRT_3JDQ0CGbW5HeFg3yK
* Distributed Computing:
https://youtube.com/playlist?list=PLPIwNooIb9vhYroMrNpoBYiBUFzTwEZot
*Gears used for this YouTube Channel:
https://linktr.ee/perfectcomputerengineer
*Let's connect:
Instagram: https://www.instagram.com/planetojas/
Material based on Jurafsky and Martin (2019): https://web.stanford.edu/~jurafsky/slp3/
Slides: http://www.natalieparde.com/teaching/cs_421_fall2020/Word%20Sense%20Disambiguation.pdf
Twitter: @NatalieParde
https://github.com/dsarchives/NLTK
Hello everyone, welcome back!
In this video, we'll learn about WSD(Word Sense Disambiguation)
In computational linguistics, word-sense disambiguation is an open problem concerned with identifying which sense of a word is used in a sentence.
NLTK use lesk algorithm for that
In natural language processing, word sense disambiguation (WSD) is the problem of determining which "sense" (meaning) of a word is activated by the use of the word in a particular context
Given an ambiguous word and the context in which the word occurs, Lesk returns a Synset with the highest number of overlapping words between the context sentence and different definitions from each Synset.
#wsd #nltk #tutorials
Word Sense Disambiguation using Princeton WordNet dictionary.
It works well on the High Performance Computing framework, neweraHPC.
Project Website http://newerahpc.com
Amii researcher (under the supervision of Amii Fellow Greg Kondrak) Yixing Luan presents "Leveraging Translations for Word Sense Disambiguation" at the AI Seminar (July 31, 2020).
The Artificial Intelligence (AI) Seminar is a weekly meeting at the University of Alberta where researchers interested in AI can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems, are explored.
Bio: Yixing Luan is a thesis-based M.Sc. candidate in Computing Science at the University of Alberta, supervised by Dr. Greg Kondrak. His research interest includes artificial intelligence and natural language processing with a focus on lexical semantics. He completed his bachelor's degree at Hokkaido University, Japan.
Abstract: Word sense disambiguation (WSD) is one of the core tasks in Natural Language Processing and its objective is to identify the correct sense of a content word in context. Although WSD is a monolingual task, it has been conjectured that multilingual information, e.g. translations, can be helpful. However, existing WSD systems rarely consider multilingual information, and no effective method has been proposed for improving WSD with machine translation. In this work, we propose methods of leveraging translations from multiple languages as a constraint to boost the accuracy of existing WSD systems. To this end, we also develop a novel knowledge-based word alignment algorithm, which outperforms an existing word alignment tool in our intrinsic and extrinsic evaluations. Since our approach is language-independent, we perform WSD experiments on standard benchmark datasets representing several languages. The results demonstrate that our methods can consistently improve the performance of various WSD systems, and obtain state-of-the-art results in both English and multilingual WSD.
The human brain is quite proficient at word-sense disambiguation. The fact that natural language is formed in a way that requires so much of it is a reflection of that neurologic reality. In other words, human language developed in a way that reflects (and also has helped to shape) the innate ability provided by the brain's neural networks. In computer science and the information technology that it enables, it has been a long-term challenge to develop the ability in computers to do natural language processing and machine learning.
To date, a rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date.