SlideShare une entreprise Scribd logo

Class14

•Télécharger en tant que PPT, PDF•
•
Dr. Cupid Lucid
Dr. Cupid Lucid
TechnologieFormation
1  sur  28
Lexical Semantics & Word Sense Disambiguation CMSC 35100 Natural Language Processing May 15, 2003
Roadmap ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Word-internal Structure ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Selectional Restrictions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Primitive Decompositions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Word Sense Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Selectional Restriction Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Selectional Restrictions: Limitations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Robust Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Disambiguation Features ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Naïve Bayes’ Approach ,[object Object],[object Object],[object Object],[object Object]
Example: “Plant” Disambiguation There are more kinds of plants and animals in the rainforests than anywhere else on Earth. Over half of the millions of known species of plants and animals live in the rainforest. Many are found nowhere else. There are even plants and animals in the rainforest that we have not yet discovered. Biological Example The Paulus company was founded in 1938. Since those days the product range has been the subject of constant expansions and is brought up continuously to correspond with the state of the art. We’re engineering, manufacturing and commissioning world- wide ready-to-run plants packed with our comprehensive know-how. Our Product Range includes pneumatic conveying systems for carbon, carbide, sand, lime and many others. We use reagent injection in molten metal for the… Industrial Example Label the First Use of “Plant”
Yarowsky’s Decision Lists: Detail ,[object Object],[object Object],[object Object],Same Sense
Yarowsky’s Decision Lists: Detail ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sense Choice With Collocational Decision Lists ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Semantic Ambiguity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Schutze’s Vector Space: Detail ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],97 Real Values
Schutze’s Vector Space: continued ,[object Object],[object Object],[object Object],[object Object],[object Object]
Sense Selection in “Word Space” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Resnik’s WordNet Labeling: Detail ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sense Labeling Under WordNet ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Question of Context ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sense
Taxonomy of Contextual Information ,[object Object],[object Object],[object Object],[object Object],[object Object]
A Trivial Definition of Context All Words within X words of Target ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Limits of Wide Context ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Surface Regularities = Useful Disambiguators ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Interactions Below the Surface ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What is Similar ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Recommandé

DataScience Lab 2017_From bag of texts to bag of clusters_Терпиль Евгений / П...
DataScience Lab 2017_From bag of texts to bag of clusters_Терпиль Евгений / П...DataScience Lab 2017_From bag of texts to bag of clusters_Терпиль Евгений / П...
DataScience Lab 2017_From bag of texts to bag of clusters_Терпиль Евгений / П...
GeeksLab Odessa
 
Fasttext(Enriching Word Vectors with Subword Information) 논문 리뷰
Fasttext(Enriching Word Vectors with Subword Information) 논문 리뷰Fasttext(Enriching Word Vectors with Subword Information) 논문 리뷰
Fasttext(Enriching Word Vectors with Subword Information) 논문 리뷰
ssuserc35c0e
 
Fasttext 20170720 yjy
Fasttext 20170720 yjyFasttext 20170720 yjy
Fasttext 20170720 yjy
재연 윤
 
Yoav Goldberg: Word Embeddings What, How and Whither
Yoav Goldberg: Word Embeddings What, How and WhitherYoav Goldberg: Word Embeddings What, How and Whither
Yoav Goldberg: Word Embeddings What, How and Whither
MLReview
 
THE WAR ON LEARNING: OGDEN & RICHARDS VS CHOMSKY
THE WAR ON LEARNING: OGDEN & RICHARDS VS CHOMSKYTHE WAR ON LEARNING: OGDEN & RICHARDS VS CHOMSKY
THE WAR ON LEARNING: OGDEN & RICHARDS VS CHOMSKY
Rodolfo Barbaste Navarro
 
Resumen Unit 3
Resumen Unit 3Resumen Unit 3
Resumen Unit 3
mjgvalcarce
 
Patterns of Semantic Integration
Patterns of Semantic IntegrationPatterns of Semantic Integration
Patterns of Semantic Integration
Optum
 
Semantics hidden within co-occurrence patterns
Semantics hidden within co-occurrence patternsSemantics hidden within co-occurrence patterns
Semantics hidden within co-occurrence patterns
Srinath Srinivasa
 

Recommandé

DataScience Lab 2017_From bag of texts to bag of clusters_Терпиль Евгений / П...
DataScience Lab 2017_From bag of texts to bag of clusters_Терпиль Евгений / П...DataScience Lab 2017_From bag of texts to bag of clusters_Терпиль Евгений / П...
DataScience Lab 2017_From bag of texts to bag of clusters_Терпиль Евгений / П...
GeeksLab Odessa
 
Fasttext(Enriching Word Vectors with Subword Information) 논문 리뷰
Fasttext(Enriching Word Vectors with Subword Information) 논문 리뷰Fasttext(Enriching Word Vectors with Subword Information) 논문 리뷰
Fasttext(Enriching Word Vectors with Subword Information) 논문 리뷰
ssuserc35c0e
 
Fasttext 20170720 yjy
Fasttext 20170720 yjyFasttext 20170720 yjy
Fasttext 20170720 yjy
재연 윤
 
Yoav Goldberg: Word Embeddings What, How and Whither
Yoav Goldberg: Word Embeddings What, How and WhitherYoav Goldberg: Word Embeddings What, How and Whither
Yoav Goldberg: Word Embeddings What, How and Whither
MLReview
 
THE WAR ON LEARNING: OGDEN & RICHARDS VS CHOMSKY
THE WAR ON LEARNING: OGDEN & RICHARDS VS CHOMSKYTHE WAR ON LEARNING: OGDEN & RICHARDS VS CHOMSKY
THE WAR ON LEARNING: OGDEN & RICHARDS VS CHOMSKY
Rodolfo Barbaste Navarro
 
Resumen Unit 3
Resumen Unit 3Resumen Unit 3
Resumen Unit 3
mjgvalcarce
 
Patterns of Semantic Integration
Patterns of Semantic IntegrationPatterns of Semantic Integration
Patterns of Semantic Integration
Optum
 
Semantics hidden within co-occurrence patterns
Semantics hidden within co-occurrence patternsSemantics hidden within co-occurrence patterns
Semantics hidden within co-occurrence patterns
Srinath Srinivasa
 
Theory Meaning pro-forma
Theory Meaning pro-formaTheory Meaning pro-forma
Theory Meaning pro-forma
Emily Whincup
 
Hxe302revision
Hxe302revisionHxe302revision
Hxe302revision
Muhammad Al Fateh
 
Sense reference
Sense referenceSense reference
Sense reference
Fadi Sukkari
 
Reference and sense
Reference and senseReference and sense
Reference and sense
Dhedeww Aruan
 
the scope of semantic
the scope of semanticthe scope of semantic
the scope of semantic
Widya' Amnezhia
 
Semantics ppt
Semantics pptSemantics ppt
Semantics ppt
Rachmad Azari
 
Unit 4 - Referring Expressions
Unit 4 - Referring ExpressionsUnit 4 - Referring Expressions
Unit 4 - Referring Expressions
Ashwag Al Hamid
 
Reference, sense and referring expression
Reference, sense and referring expressionReference, sense and referring expression
Reference, sense and referring expression
Fira Nursya`bani
 
Unit 3 - Reference and Sense
Unit 3 - Reference and SenseUnit 3 - Reference and Sense
Unit 3 - Reference and Sense
Ashwag Al Hamid
 
Unit 11 Sense Relations (2)
Unit 11 Sense Relations (2)Unit 11 Sense Relations (2)
Unit 11 Sense Relations (2)
Ashwag Al Hamid
 
Unit 10 Sense Relations (1)
Unit 10 Sense Relations (1)Unit 10 Sense Relations (1)
Unit 10 Sense Relations (1)
Ashwag Al Hamid
 
Sense relations & Semantics
Sense relations & SemanticsSense relations & Semantics
Sense relations & Semantics
Afuza Shara
 
CMSC 723: Computational Linguistics I
CMSC 723: Computational Linguistics ICMSC 723: Computational Linguistics I
CMSC 723: Computational Linguistics I
butest
 
Icon 2007 Pedersen
Icon 2007 PedersenIcon 2007 Pedersen
Icon 2007 Pedersen
University of Minnesota, Duluth
 
Measuring Similarity Between Contexts and Concepts
Measuring Similarity Between Contexts and ConceptsMeasuring Similarity Between Contexts and Concepts
Measuring Similarity Between Contexts and Concepts
University of Minnesota, Duluth
 
Word sense disambiguation and lexical chains construction using wordnet
Word sense disambiguation and lexical chains construction using wordnetWord sense disambiguation and lexical chains construction using wordnet
Word sense disambiguation and lexical chains construction using wordnet
University Politehnica Bucharest
 
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection TasksSneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks
MLconf
 
Ijcai 2007 Pedersen
Ijcai 2007 PedersenIjcai 2007 Pedersen
Ijcai 2007 Pedersen
University of Minnesota, Duluth
 
scl.ppt
scl.pptscl.ppt
scl.ppt
MariYam371004
 
Text Analytics for Semantic Computing
Text Analytics for Semantic ComputingText Analytics for Semantic Computing
Text Analytics for Semantic Computing
Meena Nagarajan
 
The Semantic Quilt
The Semantic QuiltThe Semantic Quilt
The Semantic Quilt
University of Minnesota, Duluth
 
Unsupervised Software-Specific Morphological Forms Inference from Informal Di...
Unsupervised Software-Specific Morphological Forms Inference from Informal Di...Unsupervised Software-Specific Morphological Forms Inference from Informal Di...
Unsupervised Software-Specific Morphological Forms Inference from Informal Di...
Chunyang Chen
 

Contenu connexe

En vedette

Reference, sense and referring expression
Reference, sense and referring expressionReference, sense and referring expression
Reference, sense and referring expression
Fira Nursya`bani
 
Unit 3 - Reference and Sense
Unit 3 - Reference and SenseUnit 3 - Reference and Sense
Unit 3 - Reference and Sense
Ashwag Al Hamid
 
Unit 11 Sense Relations (2)
Unit 11 Sense Relations (2)Unit 11 Sense Relations (2)
Unit 11 Sense Relations (2)
Ashwag Al Hamid
 
Unit 10 Sense Relations (1)
Unit 10 Sense Relations (1)Unit 10 Sense Relations (1)
Unit 10 Sense Relations (1)
Ashwag Al Hamid
 
Sense relations & Semantics
Sense relations & SemanticsSense relations & Semantics
Sense relations & Semantics
Afuza Shara
 

En vedette (12)

Theory Meaning pro-forma
Theory Meaning pro-formaTheory Meaning pro-forma
Theory Meaning pro-forma
 
Hxe302revision
Hxe302revisionHxe302revision
Hxe302revision
 
Sense reference
Sense referenceSense reference
Sense reference
 
Reference and sense
Reference and senseReference and sense
Reference and sense
 
the scope of semantic
the scope of semanticthe scope of semantic
the scope of semantic
 
Semantics ppt
Semantics pptSemantics ppt
Semantics ppt
 
Unit 4 - Referring Expressions
Unit 4 - Referring ExpressionsUnit 4 - Referring Expressions
Unit 4 - Referring Expressions
 
Reference, sense and referring expression
Reference, sense and referring expressionReference, sense and referring expression
Reference, sense and referring expression
 
Unit 3 - Reference and Sense
Unit 3 - Reference and SenseUnit 3 - Reference and Sense
Unit 3 - Reference and Sense
 
Unit 11 Sense Relations (2)
Unit 11 Sense Relations (2)Unit 11 Sense Relations (2)
Unit 11 Sense Relations (2)
 
Unit 10 Sense Relations (1)
Unit 10 Sense Relations (1)Unit 10 Sense Relations (1)
Unit 10 Sense Relations (1)
 
Sense relations & Semantics
Sense relations & SemanticsSense relations & Semantics
Sense relations & Semantics
 

Similaire à Class14

CMSC 723: Computational Linguistics I
CMSC 723: Computational Linguistics ICMSC 723: Computational Linguistics I
CMSC 723: Computational Linguistics I
butest
 
Word sense disambiguation and lexical chains construction using wordnet
Word sense disambiguation and lexical chains construction using wordnetWord sense disambiguation and lexical chains construction using wordnet
Word sense disambiguation and lexical chains construction using wordnet
University Politehnica Bucharest
 
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection TasksSneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks
MLconf
 
Unsupervised Software-Specific Morphological Forms Inference from Informal Di...
Unsupervised Software-Specific Morphological Forms Inference from Informal Di...Unsupervised Software-Specific Morphological Forms Inference from Informal Di...
Unsupervised Software-Specific Morphological Forms Inference from Informal Di...
Chunyang Chen
 
An Improved Approach to Word Sense Disambiguation
An Improved Approach to Word Sense DisambiguationAn Improved Approach to Word Sense Disambiguation
An Improved Approach to Word Sense Disambiguation
Surabhi Verma
 

Similaire à Class14 (20)

CMSC 723: Computational Linguistics I
CMSC 723: Computational Linguistics ICMSC 723: Computational Linguistics I
CMSC 723: Computational Linguistics I
 
Icon 2007 Pedersen
Icon 2007 PedersenIcon 2007 Pedersen
Icon 2007 Pedersen
 
Measuring Similarity Between Contexts and Concepts
Measuring Similarity Between Contexts and ConceptsMeasuring Similarity Between Contexts and Concepts
Measuring Similarity Between Contexts and Concepts
 
Word sense disambiguation and lexical chains construction using wordnet
Word sense disambiguation and lexical chains construction using wordnetWord sense disambiguation and lexical chains construction using wordnet
Word sense disambiguation and lexical chains construction using wordnet
 
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection TasksSneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks
 
Ijcai 2007 Pedersen
Ijcai 2007 PedersenIjcai 2007 Pedersen
Ijcai 2007 Pedersen
 
scl.ppt
scl.pptscl.ppt
scl.ppt
 
Text Analytics for Semantic Computing
Text Analytics for Semantic ComputingText Analytics for Semantic Computing
Text Analytics for Semantic Computing
 
The Semantic Quilt
The Semantic QuiltThe Semantic Quilt
The Semantic Quilt
 
Unsupervised Software-Specific Morphological Forms Inference from Informal Di...
Unsupervised Software-Specific Morphological Forms Inference from Informal Di...Unsupervised Software-Specific Morphological Forms Inference from Informal Di...
Unsupervised Software-Specific Morphological Forms Inference from Informal Di...
 
Managing Mature Taxonomies: Resolving Orphan Terms
Managing Mature Taxonomies: Resolving Orphan TermsManaging Mature Taxonomies: Resolving Orphan Terms
Managing Mature Taxonomies: Resolving Orphan Terms
 
Using topic modelling frameworks for NLP and semantic search
Using topic modelling frameworks for NLP and semantic searchUsing topic modelling frameworks for NLP and semantic search
Using topic modelling frameworks for NLP and semantic search
 
Natural Language Processing
Natural Language ProcessingNatural Language Processing
Natural Language Processing
 
Exempler approach
Exempler approachExempler approach
Exempler approach
 
DDH 2021-03-03: Text Processing and Searching in the Medical Domain
DDH 2021-03-03: Text Processing and Searching in the Medical DomainDDH 2021-03-03: Text Processing and Searching in the Medical Domain
DDH 2021-03-03: Text Processing and Searching in the Medical Domain
 
Aaai 2006 Pedersen
Aaai 2006 PedersenAaai 2006 Pedersen
Aaai 2006 Pedersen
 
An Improved Approach to Word Sense Disambiguation
An Improved Approach to Word Sense DisambiguationAn Improved Approach to Word Sense Disambiguation
An Improved Approach to Word Sense Disambiguation
 
Measuring Similarity Between Contexts and Concepts
Measuring Similarity Between Contexts and ConceptsMeasuring Similarity Between Contexts and Concepts
Measuring Similarity Between Contexts and Concepts
 
Combining Vocabulary Alignment Techniques
Combining Vocabulary Alignment TechniquesCombining Vocabulary Alignment Techniques
Combining Vocabulary Alignment Techniques
 
L6.pptxsdv dfbdfjftj hgjythgfvfhjyggunghb fghtffn
L6.pptxsdv dfbdfjftj hgjythgfvfhjyggunghb fghtffnL6.pptxsdv dfbdfjftj hgjythgfvfhjyggunghb fghtffn
L6.pptxsdv dfbdfjftj hgjythgfvfhjyggunghb fghtffn
 

Plus de Dr. Cupid Lucid

Types of educational_research
Types of educational_researchTypes of educational_research
Types of educational_research
Dr. Cupid Lucid
 
History of english literature sajid
History of english literature sajidHistory of english literature sajid
History of english literature sajid
Dr. Cupid Lucid
 
A guide to_writing_research_papers
A guide to_writing_research_papersA guide to_writing_research_papers
A guide to_writing_research_papers
Dr. Cupid Lucid
 
101 masterpieces of literature in english
101 masterpieces of literature in english101 masterpieces of literature in english
101 masterpieces of literature in english
Dr. Cupid Lucid
 
National Curriculum of English Grade I-XII
National Curriculum of English Grade I-XIINational Curriculum of English Grade I-XII
National Curriculum of English Grade I-XII
Dr. Cupid Lucid
 
The Linguistic Variables
The Linguistic VariablesThe Linguistic Variables
The Linguistic Variables
Dr. Cupid Lucid
 
Research Proposal Methoo
Research Proposal MethooResearch Proposal Methoo
Research Proposal Methoo
Dr. Cupid Lucid
 

Plus de Dr. Cupid Lucid (20)

Teaching English for specific purposes
Teaching English for specific purposesTeaching English for specific purposes
Teaching English for specific purposes
 
Science and approaches of science
Science and approaches of scienceScience and approaches of science
Science and approaches of science
 
Content Analysis vs secondary analysis
Content Analysis vs secondary analysisContent Analysis vs secondary analysis
Content Analysis vs secondary analysis
 
Basic elements of scientific concepts
Basic elements of scientific conceptsBasic elements of scientific concepts
Basic elements of scientific concepts
 
Observational methods
Observational methodsObservational methods
Observational methods
 
Types of educational_research
Types of educational_researchTypes of educational_research
Types of educational_research
 
Types of research
Types of researchTypes of research
Types of research
 
Types of Research
Types of ResearchTypes of Research
Types of Research
 
Literature what is it
Literature what is itLiterature what is it
Literature what is it
 
History of english literature sajid
History of english literature sajidHistory of english literature sajid
History of english literature sajid
 
A guide to_writing_research_papers
A guide to_writing_research_papersA guide to_writing_research_papers
A guide to_writing_research_papers
 
What isliterature
What isliteratureWhat isliterature
What isliterature
 
101 masterpieces of literature in english
101 masterpieces of literature in english101 masterpieces of literature in english
101 masterpieces of literature in english
 
National Curriculum of English Grade I-XII
National Curriculum of English Grade I-XIINational Curriculum of English Grade I-XII
National Curriculum of English Grade I-XII
 
The Linguistic Variables
The Linguistic VariablesThe Linguistic Variables
The Linguistic Variables
 
Term Paper
Term PaperTerm Paper
Term Paper
 
Syllabus Designing
Syllabus DesigningSyllabus Designing
Syllabus Designing
 
Semiotics Final
Semiotics FinalSemiotics Final
Semiotics Final
 
Research Proposal Methoo
Research Proposal MethooResearch Proposal Methoo
Research Proposal Methoo
 
Questionnaire
QuestionnaireQuestionnaire
Questionnaire
 

Dernier

Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Paige Cruz
 
Syngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdf
Syngulon
 
UiPath manufacturing technology benefits and AI overview
UiPath manufacturing technology benefits and AI overviewUiPath manufacturing technology benefits and AI overview
UiPath manufacturing technology benefits and AI overview
DianaGray10
 
ChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps ProductivityChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps Productivity
VictorSzoltysek
 
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider Progress from Awareness to Implementation.pptxTales from a Passkey Provider Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
FIDO Alliance
 
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Skynet Technologies
 

Dernier (20)

How to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cfHow to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cf
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024
 
Google I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGoogle I/O Extended 2024 Warsaw
Google I/O Extended 2024 Warsaw
 
Design Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxDesign Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptx
 
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
 
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsContinuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
 
Syngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdf
 
UiPath manufacturing technology benefits and AI overview
UiPath manufacturing technology benefits and AI overviewUiPath manufacturing technology benefits and AI overview
UiPath manufacturing technology benefits and AI overview
 
Portal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russePortal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russe
 
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
 
ChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps ProductivityChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps Productivity
 
State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
 
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider Progress from Awareness to Implementation.pptxTales from a Passkey Provider Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
 
Introduction to FIDO Authentication and Passkeys.pptx
Introduction to FIDO Authentication and Passkeys.pptxIntroduction to FIDO Authentication and Passkeys.pptx
Introduction to FIDO Authentication and Passkeys.pptx
 
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
 
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
 
Cyber Insurance - RalphGilot - Embry-Riddle Aeronautical University.pptx
Cyber Insurance - RalphGilot - Embry-Riddle Aeronautical University.pptxCyber Insurance - RalphGilot - Embry-Riddle Aeronautical University.pptx
Cyber Insurance - RalphGilot - Embry-Riddle Aeronautical University.pptx
 
JavaScript Usage Statistics 2024 - The Ultimate Guide
JavaScript Usage Statistics 2024 - The Ultimate GuideJavaScript Usage Statistics 2024 - The Ultimate Guide
JavaScript Usage Statistics 2024 - The Ultimate Guide
 
2024 May Patch Tuesday
2024 May Patch Tuesday2024 May Patch Tuesday
2024 May Patch Tuesday
 

Class14

  • 1. Lexical Semantics & Word Sense Disambiguation CMSC 35100 Natural Language Processing May 15, 2003
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12. Example: “Plant” Disambiguation There are more kinds of plants and animals in the rainforests than anywhere else on Earth. Over half of the millions of known species of plants and animals live in the rainforest. Many are found nowhere else. There are even plants and animals in the rainforest that we have not yet discovered. Biological Example The Paulus company was founded in 1938. Since those days the product range has been the subject of constant expansions and is brought up continuously to correspond with the state of the art. We’re engineering, manufacturing and commissioning world- wide ready-to-run plants packed with our comprehensive know-how. Our Product Range includes pneumatic conveying systems for carbon, carbide, sand, lime and many others. We use reagent injection in molten metal for the… Industrial Example Label the First Use of “Plant”
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.