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Wordnet Introduction

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.

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WORDNET Reporter: Nguyen Duc Minh Khoi @ Ho Chi Minh City University of Technology Thursday, November 01, 2012
11/1/2012 WordNet Report 2 Contents Intro to WordNet Nouns Modifiers Verbs WordNet system
11/1/2012 WordNet Report 3 INTRODUCTION TO WORDNET
11/1/2012 WordNet Report 4 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
11/1/2012 WordNet Report 5 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
11/1/2012 WordNet Report 6 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
11/1/2012 WordNet Report 7 NOUNS
11/1/2012 WordNet Report 8 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
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:
11/1/2012 WordNet Report 10 Lexical hierarchy (cont.) • Examples:
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}
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
11/1/2012 WordNet Report 13 MODIFIERS
11/1/2012 WordNet Report 14 Adjectives • Main functions: modifying nouns • Types: • Descriptive adjectives • Participle adjectives • Relational adjectives • Format: • A(x) = adj • E.g.: WEIGHT(package) = heavy
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
11/1/2012 WordNet Report 16 Adjectives Relations (cont.) • Other relations • Troponym (~): • Hypernym (@): • Entailment (*): • Cause (>): • Also see (^):
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:
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
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
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
11/1/2012 WordNet Report 21 Adverbs • Derived from adjectives by suffixation: • -ly: • Specify manner: e.g.: beautifully • Specify degree: e.g.: extremely • Other suffix: • -wise, -way, -ward • E.g.: northward, forward • Inherit their adjectives about: • Antonym • Gradation
11/1/2012 WordNet Report 22 VERBS
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)
11/1/2012 WordNet Report 24 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
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
11/1/2012 WordNet Report 26 WORDNET SYSTEM
11/1/2012 WordNet Report 27 WordNet system
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
11/1/2012 WordNet Report 29 Sample Application use WordNet • NLTK is a platform for building Python programs to work with human language data • Sample commands: • Work with nouns:
11/1/2012 WordNet Report 30 Sample Application use WordNet (cont.) • Work with verbs