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Generative Lexicon- Idea and Practicality. Debasri Chakrabarti 02408601 Guide : Prof.Milind S. Malshe Co-Guide : Prof. Pushpak Bhattacharyya. Overview. Introduction Polysemy and the Logical Problem of Polysemy Generative Lexicon Theory Lexicon Building

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generative lexicon idea and practicality

Generative Lexicon- Idea and Practicality

Debasri Chakrabarti

02408601

Guide: Prof.Milind S. Malshe

Co-Guide: Prof. Pushpak Bhattacharyya

overview
Overview
  • Introduction
  • Polysemy and the Logical Problem of Polysemy
  • Generative Lexicon Theory
  • Lexicon Building
  • Applications and Limitations of GLT
  • Conclusion
introduction
Introduction
  • Lexicon— ideally collection of all words of a language
  • Information stored in a lexicon-
        • Phonetic information
          • pronunciation
        • Semantic information
          • meaning
        • Morphological information
          • transitivity and intransitivity (verbs) , count vs. mass (noun)
lexicon contd
Lexicon (contd…)

Example of “eat” in the Oxford Advanced Learner’s Dictionary

eat /i:t/ v (pt ate /et/; pp eaten /i:tn/):1. sth (up) to food into the mouth,chew and swallow it: he was too ill to eat

Pronunciation

Meaning

Morphological information

Lexical entry

mental lexicon
Mental Lexicon
  • Mental Lexicon: information stored in the mind of a native speaker
  • Native speakers store information
    • Phonetic information
        • pronunciation
    • Semantic information
        • meaning
    • Morphological information
        • transitivity vs.intransitivity (verbs), count vs. mass (noun)
  • Additional information
        • use of a word in a new context, syntactic environment of a word, word-formation rules
example of mental lexicon
Example of Mental Lexicon
  • Example of eat in a native speaker’s mind
  • Pronunciation: long /i:/ is used in eat
  • Grammatical information: past tense is ate /et/
  • Word-formation rules: /-s/ is the third person singular present tense marker as in
  • he eats
  • Meaning: 1. Take in solid food: she ate a banana
  • 2. Take a meal: we did not eat until 10 P.M.
  • 3. Worry or cause anxiety in a persistent way: what’s eating you up.
  • Syntactic Information: eat needs an agent to perform the action.
  • the agent role is obligatory.
lexicon in computational linguistics
Lexicon in Computational Linguistics
  • Lexicon meant for Natural Language Processing (NLP) must have the
  • following properties:
  • Morphological information
      • Parts of speech information
      • Rules should be there to deal with both regular and irregular forms
      • e.g ate (past tense of eat)
      • men (plural of man)
  • Semantic information
      • Can handle lexical ambiguity
  • Syntactic information
      • Action verbs will always have an agent
polysemy and the logical problem of polysemy
Polysemy and the Logical Problem of Polysemy

Polysemy

  • An individual word can have indefinite number of subtle meaning difference
  • Natural Languages are highly polysemous
  • This creates ambiguity
  • Weinreich distinguishes between two types of ambiguity
      • Contrastive ambiguity
      • Complementary polysemy
polysemy and the logical problem of polysemy contd
Polysemy and the Logical Problem of Polysemy (contd…)

Contrastive Ambiguity

  • A lexical item carries two distinct unrelated meanings
  • This is a case of homonymy
        • words spelled or pronounced in the same way but have different

meanings

Example:

    • bank a financial institution
    • bank place beside a body of water.
polysemy and the logical problem of polysemy contd1
Polysemy and the Logical Problem of Polysemy (contd…)

Complementary Polysemy

  • Manifestation of the same basic sense
  • Denotes a relation among different senses

Example,

      • John crawled through the window.
      • The window is closed.

Sense 1. Apparatus

Sense 2. Physical Object

sense enumeration lexicon sel
Sense Enumeration Lexicon (SEL)
  • Simplest model of lexical design to capture the logical polysemy.
  • Widely accepted in both computational and theoritical linguistics.
  • Direct approach to handle polysemy is to allow the lexicon to have multiple listing of words, each annotated with a separate meaning or lexical sense.
sense enumeration lexicon sel1
Sense Enumeration Lexicon (SEL)
  • Example of Contrastive Senses

bank2

CAT= count-noun

GENUS= shore

bank1

CAT= count-noun

GENUS= financial-institution

sense enumeration lexicon sel2

Window1

CAT= count-noun

GENUS= apparatus

Sense Enumeration Lexicon (SEL)
  • Example of Complementary Polysemy

Window2

CAT= count-noun

GENUS= artifact

sense enumeration lexicon sel3
Sense Enumeration Lexicon (SEL)
  • Possible Modification of Complementary Polysemy in SEL

window

sense1

CAT= count-noun GENUS= apparatus

sense2

CAT= count-noun GENUS= artifact

generative lexicon theory glt
Generative Lexicon Theory(GLT)
  • Major Problems for Lexical Semantics
      • to explain the polymorphic nature of language
      • to characterize the semanticality of natural language utterances
      • to capture the creative use of words in novel contexts
      • to develop a richer, co-compositional semantic representation
  • Generative Lexicon Theory
      • developed by James Pustejovsky
      • crucial aspect of GLT is the representation and treatment of polysemy
      • it examines the meaning of words to see the range of polysemy
methodology of generative lexicon theory
Methodology of Generative Lexicon Theory

Generative lexicon involves the following methodology

  • Argument Structure
        • True Arguments
        • Default Arguments
        • Shadow Arguments
        • True Adjuncts
  • Event Structure
  • Qualia Structure
        • Formal
        • Constitutive
        • Telic
        • Agentive
argument structure
Argument Structure
  • True Arguments: syntactically realized parameters of the lexical item

John arrived late

  • Default Arguments: logically present in the expressions but are not necessarily expressed syntactically.

John built the house out of bricks

  • True Adjuncts:
        • modify the logical expression
        • part of the situational interpretation

She drove down to New York on Tuesday.

argument structure contd
Argument Structure (contd…)
  • Shadow Arguments:semantically incorporated in the lexical item and are expressed by discourse specification and contextual factors

Mary buttered her toast

      • hidden argument is the material being spread on the toast
      • these are not optional arguments but expressible only under specific conditions
      • refer to the semantic content that is not necessarily expressed in syntax

Example: Mary buttered her toast with margarine

event structure
Event Structure
  • event type of a lexical item and a phrase
  • events can be sub-classified into at least three sorts: State, Process and Transition

Event Structure of build as found in the following expressions

They are building a new house

The house was built by John

build

EVENTSTR=

E1= process

E2= state

qualia structure
Qualia Structure
  • gives a relational force for a lexical item
  • composed of four qualia roles
      • Formal:This qualia role distinguishes a lexical item within a

larger domain.

      • Constitutive: This is a relation between an object and its constituent

parts.

      • Telic:This specifies the purpose and function of a lexical item.
      • Agentive:This indicates the factors involved in the origin of a

lexical item.

qualia structure contd

novel

const = narrative

formal = book

telic = reading

agent = writing

Qualia

Qualia Structure (contd…)

Qualia Structure for novel

lexical conceptual paradigm lcp
Lexical Conceptual Paradigm (LCP)
  • The term is used by Pustejovsky and Anick (1988)
  • Refers to the ability of a lexical item to cluster multiple senses

Example,

      • John crawled through the window.
      • The window is closed.
  • Resulting LCP
      • phys-obj.aperture-lcp = [phys-obj]

[aperture]

generative device
Generative Device
  • Type Coercion
      • a lexical item or phrase is coerced to a semantic interpretation by a governing item in the phrase, without changing its syntactic type

Mary wants John to leave

Mary wants to leave

Mary wants the book

  • Function Application with Coercion
      • different complement type of the verb
      • different interpretations of the verb that arise for the different

complements

generative device1
Generative Device
  • Selective Binding
      • a lexical item or a phrase operates specifically on the substructure of a phrase, without changing the overall type in the composition

a good knife: a knife that cuts well

  • Co-composition
      • multiple elements within a phrase behave as functors, generating new non-lexicalized senses for the words in composition

John baked the potato

John baked the cake

lexicon building
Lexicon Building
  • Building of WordNet
      • lexical database organised in terms of concept
      • each concept is related to each other in terms of various semantic relations
  • Building of a Universal Word Dictionary
      • building a lexicon for Universal Networking Language
      • Universal Networking Language (UNL) is an electronic language

for computers to express and exchange all kinds of information

  • Creation of Verb hierarchy Tree
      • creating a verb knowledge base for the UNL system
building of wordnet
Building of WordNet
  • Different semantic relations in WordNet
      • Synonymy
      • Antonymy
      • Hypernymy and Hyponymy
      • Meronymy and Holonymy
      • Entailment and Troponymy
  • Multiple Hypernymy in Euro WordNet
      • Disjunctive Hypernym
      • Conjunctive Hypernym
      • Nonexclusive Hypernym
building of wordnet1
Building of WordNet
  • Disjunctive Hypernym
      • these are incompatible types that never apply simultaneously
      • found amongnouns that refer to the participant in an event

but do not restrict for the type of entity participating

threat

- Role- Agent threaten

- Has Hypernym person; disjunctive

- Has Hypernym thing; disjunctive

- Has Hypernym idea; disjunctive

building of wordnet2
Building of WordNet
  • Conjunctive Hypernym
      • these are compatible types that always apply simultaneously
      • found for verbs in which multiple aspects are combined.
      • Dutch Example

doodschoppento kick to death

- Has Hypernym doden (to kill); conjunctive

- Has Hypernym schoppen (to kick); conjunctive

  • Similar Hindi example

huMkarnaa: Dranao ko ilae jaaor ka Sabd krnaa(to shout to scare somebody)

  - Has Hypernym Dranaa (to scare) conjunctive

- Has Hypernym icallaanaa (to shout)conjunctive

building of wordnet3
Building of WordNet
  • Non-exclusive Hypernym
      • either both aspects may apply simultaneously or one of both may

apply

knife

- Has Hypernym weapon

- Has Hypernym cutlery

building of a universal word dictionary
Building of a Universal Word Dictionary
  • Construction of Universal Word (UW) in Universal Networking Language (UNL)
  • UNL – electronic language for computers to express and exchange all kinds of
  • information
  • UW – character strings representing unique concept
  • eat (icl>consume) as in he is eating
  • eat (icl> damage) as in the house was eaten up by the heat
  • represented by an English word
  • captures all the meanings conveyed by that word
  • restrictions are attached to create unique sense
  • UNL Knowledge Base (KB)— performs the task of defining all possible
  • relationships between two UWs.
how to create an uw
How to create an UW

I. First a category is decided

a. nominal concept (icl> thing) is attached

e.g swallow(icl> thing)

b. verbal concept

(icl>do) concept of an event caused by something or someone

change (icl>do) as in I changed my mind.

(icl>occur) concept of an event that happens of its own accord

change (icl>occur) as in The weather will change.

(icl>be) concept of a state verb

know(icl>be) as in I know you.

how to create a uw contd
How to create a UW(contd…)
  • To handle the ambiguity of a UW
  • For a nominal concept, a subordinate category from the uw hierarchy
  • should be used rather than a thing.
  • Example: swallow (icl>bird) as in the swallow is singing.
  • swallow(icl>action) as in he took the drink at [in] one swallow.
  • swallow(icl>quantity) as in take a swallow of water.
  • For a verbal concept possible case relations are attached.
  • case relations are like obj>thing, obj>person, gol>thing
  • Example: spring(icl>occur(obj>liquid)): expresses gushing out as in to spring out
  • spring(icl>do(gol>place)): expresses jumping up as in to spring up
creation of a verb hierarchal tree
Creation of a verb hierarchal tree

Creation of the Verb knowledge base

Following :

1.Beth Levin’s methodology of verb alternation

example, a. Bill sold a car.

b. Bill sold Tom a car.

2. Hypernymy relation of English Wordnet

Hypernym denotes superset of a concept

example,

animal

Hypernym

cat

creation of a verb hierarchal tree contd
Creation of a verb hierarchal tree contd…
  • Beth Levin gives the syntactic information.
  • Hypernymy gives the semantic information.
  • The classification is in the following manner:
      • "do(agt>thing,obj>thing {,gol>thing,src>thing,icl>do})"
      • "argue({icl>do(}agt>thing,obj>thing,ptn>thing{)})"
creation of a verb hierarchal tree contd1
Creation of a verb hierarchal tree contd…

Format of the entry:

1Tab "attack({icl>do(}agt>thing,obj>thing{)})"; Most wild animals won't attack humans unless they are provoked. /Army forces have been attacking (the town) since dawn with mortar and shell fire. / Napoleon attacked Russia in 1812 and was defeated and forced to retreat. (to make an attack on sb/sth)

2Tab Tab"assault(icl>attack(agt>thing,obj>thing,man>emotionally))" Nightmares assaulted him regularly.(to attack sb emotionally)

2Tab Tab"assault(icl>attack(agt>thing,obj>thing,man>physically))" ;He got two year's imprisonment for assaulting a police officer.[Vn](to attack sb physicaly and violently, esp when this is a crime)

application of glt
Application of GLT
  • Formal role is similar with the hypernymy relation
  • Constitutive role is similar with the meronymy

relation

  • Telic role is similar with the functional link given between a Noun and a Verb in the Hindi WordNet
  • LCP is used in the multi hypernymy process
  • Event structure is specified by the ontology nodes in the Hindi WordNet
application of glt1
Application of GLT
  • English Wordnet (1.7.1) gives 63 senses for the verb sense of break

interrupt, break 1-- (end prematurely; break a lucky streak)

break, break off, discontinue, stop 10-- (prevent completion; stop the project; break the silence)

break, break away18-- (interrupt a continued activity; She had broken with the traditional patterns)

break31-- (stop or interrupt; He broke the engagement; We had to break our plans for a trip to China)

separate, part, split up, split, break, break up 33-- (discontinue an association or relation; go different ways; The business partners broke over a tax question; The couple separated after 25 years of marriage; My friend and I split up)

application of glt2

Break

EVENTSTR

QUALIA

E: event

FORMAL: interruption

AGENTIVE: break_act

Application of GLT
  • Merging of senses using GLT
limitations of glt
Limitations Of GLT
  • Attempts to distinguish between polysemy and accidental homonymy

Example of bake

        • baked a cake (creativity)
        • baked a potato (change of state)
  • Pustejovsky’s suggestion
        • cake-artifact
        • potato-nat obj

Problem: how to deal with artifacts like knife, car?

conclusion
Conclusion
  • Generative mechanisms fail to predict polysemy or
  • generate polysemous sense
  • Generative mechanisms along with ontology can be a
  • powerful device
  • This implies the building of a rich ontology