Wordnet Introduction

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


INTRODUCTION TO WORDNET


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


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


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


NOUNS


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


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:


Lexical hierarchy (cont.)
• Examples:


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}


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


MODIFIERS


Adjectives
• Main functions: modifying nouns
• Types:
• Descriptive adjectives
• Participle adjectives
• Relational adjectives
• Format:
• A(x) = adj
• E.g.: WEIGHT(package) = heavy


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


Adjectives Relations (cont.)
• Other relations
• Troponym (~):
• Hypernym (@):
• Entailment (*):
• Cause (>):
• Also see (^):


Gradation
• Contrary: one of propositions can be true or both are false
• Adjectives can be use to express different level of action
• For example:


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


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


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


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


VERBS


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)


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


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


WORDNET SYSTEM


WordNet system


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


Sample Application use WordNet
• NLTK is a platform for building Python programs to work
with human language data
• Sample commands:
• Work with nouns:


Sample Application use WordNet (cont.)

• Work with verbs

Wordnet Introduction

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