The document provides an overview of WordNet, a lexical database for the English language. It discusses the main components of WordNet including nouns, verbs, adjectives and its organization. WordNet groups words into sets of synonyms and defines semantic relationships between these word sets. It contains lexical and semantic information that can be used in natural language processing tasks.
word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.
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Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
WordNet:The most well-developed and widely used lexical DB for English.Handcrafting from scratch, rather than mining information from existing dictionaries and thesauri
Consisting three separate DBs:One each for nouns and verbs, and A third for adjectives and adverbs.
word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
WordNet:The most well-developed and widely used lexical DB for English.Handcrafting from scratch, rather than mining information from existing dictionaries and thesauri
Consisting three separate DBs:One each for nouns and verbs, and A third for adjectives and adverbs.
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This presentation I initially presented at Data Science UA meetup in August, 2018. Link to the video: https://www.youtube.com/watch?v=Ksg_36ljcQ8&feature=youtu.be&app=desktop&fbclid=IwAR0YQ_WR2YlBLrLSCcLWmV2WviVF1Eo4KB6YCu7C5HNCpCrhEwO-1AIbGqE.
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2. 11/1/2012 WordNet Report 2
Contents
Intro to WordNet
Nouns
Modifiers
Verbs
WordNet system
3. 11/1/2012 WordNet Report 3
INTRODUCTION TO WORDNET
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Overview
• WordNet is lexical database for the English language that
groups English word into set of synonyms called synset
• Authors: the Cognitive Science Laboratory of Princeton
University under the direction of psychology professor
George A. Miller
• Used by:
• Linguistics Scientist
• Psychologist
• Artificial intelligence Scientist
• Natural Language Processing Scientist
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Contents of WordNet
• WordNet distinguish between nouns, verbs, adjectives,
adverbs – 4 major syntactic categories
• WordNet contains basic units:
• Compounds
• Phrasal verbs
• Collocations
• Idiomatic phrases
• WordNet as a dictionary:
• Give definitions
• Sample sentences
• Contains synonym sets
• WordNet as a thesaurus:
• Conceptual level: semantic conceptual relations
• Lexical level: lexical relation
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Other information
• Lexical database can be built by:
• Automatic acquisition
• Craft one dictionary by hand
• Knowledge engineering:
• Lexical level: contains information about synonyms, antonyms...
• Domain level: refer to the topic of discourse
• Application specific level: relates objects and events
• Tennis problem:
• Contains no relations that indicate the word’s shared membership in a
topic of discourse
• E.g. not link racquet, ball, net => court game
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Introduction to nouns in WordNet
• WordNet is machine readable dictionary
• Noun in WordNet doesn’t give:
• pronunciation
• Derivative morphology
• Etymology
• Usage notes
• Pictorial illustration
• WordNet try to make semantic relations by extract synonym
from thesaurus manually
• WordNet lexicalized concept by making synset relate to that
concept
9. 11/1/2012 WordNet Report 9
Lexical hierarchy
• Tree graph: graph without circular loop
• Assumptions:
• Longer distance in hierarchy longer traverse in thoughts
• More lexical information must be stored in every lexicalized concepts
than is required to establish in hierarchy.
• Noun’s unique beginner:
11. 11/1/2012 WordNet Report 11
Noun relations
• Hyponyms (~):
• A word of more specific meaning than a general or superordinate term
applicable to it.
• For example, {bowl} is a hyponym of {dish}: {bowl} ~-> {dish}
• Hypernyms (@):
• A word with a broad meaning that more specific words fall under; a
superordinate.
• For example, {color} is a hypernym of {red}: {color} @-> {red}
• Meronyms (#):
• The semantic relation that holds between a part and the whole.
• For example, {beak} and {wing} are meronyms of {bird}: {beak, wing} #-> bird
• Three kinds: component, member, made from
• Holonyms (%):
• The semantic relation that holds between a whole and its parts
• For example, {building} is a holonym of {window}: {building} %-> {window}
12. 11/1/2012 WordNet Report 12
Noun relations (cont.)
• Antonyms (!):
• A word opposite in meaning to another
• For example, {man} !-> {woman}
• Polysemous nouns:
• Nous that have many meanings
• For example, {mouse} living animal or computer device
• Rules: two meanings of a word are similar then the meaning of their
hyponyms should also be similar in the same way.
• Attribute (=) and modifications:
• Values of attribute are expressed by adjectives
• Modification can also be nouns
• For examples, chair -> small chair, big chair
15. 11/1/2012 WordNet Report 15
Adjectives Relations
• Antonyms (!):
• Basic semantic relation among descriptive adjectives
• Means “IS ANOYNYMOUS TO”, e.g. heavy is anonymous to light
• Can be direct, e.g. heavy/light
• Or can be indirect, e.g. heavy/airy
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Adjectives Relations (cont.)
• Other relations
• Troponym (~):
• Hypernym (@):
• Entailment (*):
• Cause (>):
• Also see (^):
17. 11/1/2012 WordNet Report 17
Gradation
• Contrary: one of propositions can be true or both are false
• Adjectives can be use to express different level of action
• For example:
18. 11/1/2012 WordNet Report 18
Other stuffs
• Markedness:
• Normal linguistic unit (unmarked term) compare to unit possible
irregular forms (marked term)
• E.g.: The pool is 5 feet deep, NOT: The pool is 5 feet shallow
• So deep marked term, shallow unmarked term
• Polysemy and selectional preferences:
• E.g.: old can be not young modify persons
old can be not new modify things
• Some adjectives can modify almost any nouns
• E.g.: good / bad, desirable / undesirable
• Some adjectives can strictly restricted to some nouns
• E.g.: editable / ineditable
19. 11/1/2012 WordNet Report 19
Other types of descriptive adjectives
• Color adjectives:
• Server as nouns and adjectives
• Quantifiers:
• E.g.: all, some, many, few…
• Participle adjectives:
• Means “PRINCIPLE PART OF”
• E.g.: breaking is principle part of break
• Can be –ing/-ed: running water, elapsed time
20. 11/1/2012 WordNet Report 20
Relational adjectives
• Differ from descriptive adjectives by
• Do not relate to attribute of nouns
• Can not be gradable
• Occur only attribute position
• Lack of direct antonym
• E.g.: criminal behavior
23. 11/1/2012 WordNet Report 23
Organizations
• Types of semantic verbs:
• motion, perception, communication, competition, change, cognitive,
consumption, creation, emotion, possession, body care, functions, social
behavior, interaction.
• Stative verb:
• Collaborate with be: resemble, belong, suffice
• Control verb: want, fail, prevent, succeed, begin
• Cannot group all verbs in unique beginner like nouns
• English has fewer verb than nouns BUT approximate twice as
polysemous as noun
• Verb synset:
• Synonym and near synonym: e.g.: pass away vs. die vs. kick the bucket
• Idiom and metaphors:
• Kick the bucket include synset
• Die include synonym: break, break down (for car and computer)
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Verb Relations
• Entailment (*):
• The verb Y is entailed by X if by doing X you must be doing Y
• E.g.: to snore entails to sleep
• Not mutual: V1 * V2 NOT V2 V1
25. 11/1/2012 WordNet Report 25
Verb relations
• Troponym (~):
• The verb Y is a troponym of the verb X if the activity Y is doing X in
some manner
• E.g.: to lisp is a troponym of to talk
• Special case of entailment
• Most frequently coded in WordNet
• Antonym (!):
• E.g.: give/take, buy/sell, lend/borrow, teach/learn
• Can also be troponym: fail/succeed entails try, forget entails know
• Hypernym (@):
• The verb Y is a hypernym of the verb X if the activity X is a (kind of) Y
• E.g.: to perceive is an hypernym of to listen
28. 11/1/2012 WordNet Report 28
Lexical files
• WordNet store nouns, adjectives, adverbs and nouns into
synset lexical source files by syntactic categories
• Nouns and verbs: grouped according to semantic fields
• Adjectives are divided among three files (adj.all, adj.ppl, adj.pert)
• Adverb are store in single file
• Relation pointers store in WordNet
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Sample Application use WordNet
• NLTK is a platform for building Python programs to work
with human language data
• Sample commands:
• Work with nouns:
30. 11/1/2012 WordNet Report 30
Sample Application use WordNet (cont.)
• Work with verbs