1 Generative Lexicon- Idea and Practicality Debasri Chakrabarti 02408601 Guide: Prof.Milind S. Malshe Co-Guide: Prof. Pushpak Bhattacharyya

1

Generative Lexicon- Idea and PracticalityGenerative Lexicon- Idea and Practicality

Debasri Chakrabarti

02408601

Guide: Prof.Milind S. Malshe

Co-Guide: Prof. Pushpak Bhattacharyya

2

OverviewOverview

• Introduction

• Polysemy and the Logical Problem of Polysemy

• Generative Lexicon Theory

• Lexicon Building

• Applications and Limitations of GLT

• Conclusion

3

IntroductionIntroduction

• 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)

4

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

Lexical entry

Pronunciation

Morphological informationMeaning

5

Mental LexiconMental 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

6

Example of Mental LexiconExample 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.

7

Lexicon in Computational LinguisticsLexicon 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

8

Polysemy and the Logical Problem of Polysemy and the Logical Problem of PolysemyPolysemy

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

9

Polysemy and the Logical Problem of Polysemy and the Logical Problem of Polysemy (contd…)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.

10

Polysemy and the Logical Problem of Polysemy and the Logical Problem of Polysemy (contd…)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

11

Sense Enumeration Lexicon (Sense Enumeration Lexicon (SELSEL))

• 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.

12


• Example of Contrastive Senses

bank1

CAT= count-noun GENUS= financial-institution

bank2

CAT= count-noun GENUS= shore

13


• Example of Complementary Polysemy

Window2

CAT= count-noun GENUS= artifact

Window1

CAT= count-noun GENUS= apparatus

14


• Possible Modification of Complementary Polysemy in SEL

CAT= count-noun GENUS= artifact

CAT= count-noun GENUS= apparatus

sense1

sense2

window

15

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

16

Methodology of Generative Lexicon Methodology of Generative Lexicon TheoryTheory

Generative lexicon involves the following methodology

• Argument Structure True Arguments

Default Arguments

Shadow Arguments

True Adjuncts

• Event Structure

• Qualia Structure Formal

Constitutive

Telic

Agentive

17

Argument StructureArgument 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.

18

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

19

Event StructureEvent 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= processE2= state

20

Qualia StructureQualia 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.

21

Qualia Structure (contd…)Qualia Structure (contd…)

Qualia Structure for novel

novel

const = narrativeformal = booktelic = readingagent = writingQualia

22

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]

23

Generative DeviceGenerative 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

24

Generative DeviceGenerative 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

25

Lexicon BuildingLexicon 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

26

Building of WordNetBuilding 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

27


• Disjunctive Hypernym these are incompatible types that never apply simultaneously found among nouns 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

28


• Conjunctive Hypernym these are compatible types that always apply simultaneously found for verbs in which multiple aspects are combined.

• Dutch Example

doodschoppen to 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

29


• Non-exclusive Hypernym either both aspects may apply simultaneously or one of both may

apply

knife

- Has Hypernym weapon

- Has Hypernym cutlery

30

Building of a Universal Word Building of a Universal Word DictionaryDictionary

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.

31

How to create an UWHow 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.

32

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

33

Creation of a verb hierarchal treeCreation 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.a. Bill sold a car.

b. Bill sold Tom a car.b. Bill sold Tom a car.

2. Hypernymy relation of English Wordnet

Hypernym denotes superset of a concept

example,

cat

animal

Hypernym

34

Creation of a verb hierarchal tree Creation of a verb hierarchal tree contd…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{)})"

35

Creation of a verb hierarchal tree Creation of a verb hierarchal tree contd…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)

2TabTab"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)

36

Application of GLTApplication 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

37


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 away 18-- (interrupt a continued activity; She had broken with the traditional patterns)

break 31-- (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)

38


• Merging of senses using GLT

Break

EVENTSTR

QUALIA

E: event

FORMAL: interruptionAGENTIVE: break_act

39

Limitations Of GLTLimitations 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?

40

ConclusionConclusion

• 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

Documents

1 Generative Lexicon- Idea and Practicality Debasri Chakrabarti 02408601 Guide: Prof.Milind S. Malshe Co-Guide: Prof. Pushpak Bhattacharyya