1 generative lexicon- idea and practicality debasri chakrabarti 02408601 guide: prof.milind s....
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Generative Lexicon- Idea and PracticalityGenerative Lexicon- Idea and Practicality
Debasri Chakrabarti
02408601
Guide: Prof.Milind S. Malshe
Co-Guide: Prof. Pushpak Bhattacharyya
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OverviewOverview
• Introduction
• Polysemy and the Logical Problem of Polysemy
• Generative Lexicon Theory
• Lexicon Building
• Applications and Limitations of GLT
• Conclusion
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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)
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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
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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
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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.
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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
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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
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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.
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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
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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.
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Sense Enumeration Lexicon (Sense Enumeration Lexicon (SELSEL))
• Example of Contrastive Senses
bank1
CAT= count-noun GENUS= financial-institution
bank2
CAT= count-noun GENUS= shore
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Sense Enumeration Lexicon (Sense Enumeration Lexicon (SELSEL))
• Example of Complementary Polysemy
Window2
CAT= count-noun GENUS= artifact
Window1
CAT= count-noun GENUS= apparatus
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Sense Enumeration Lexicon (Sense Enumeration Lexicon (SELSEL))
• Possible Modification of Complementary Polysemy in SEL
CAT= count-noun GENUS= artifact
CAT= count-noun GENUS= apparatus
sense1
sense2
window
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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
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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
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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.
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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
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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
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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.
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Qualia Structure (contd…)Qualia Structure (contd…)
Qualia Structure for novel
novel
const = narrativeformal = booktelic = readingagent = writingQualia
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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]
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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
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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
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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
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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
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Building of WordNetBuilding of WordNet
• 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
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Building of WordNetBuilding of WordNet
• 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
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Building of WordNetBuilding of WordNet
• Non-exclusive Hypernym either both aspects may apply simultaneously or one of both may
apply
knife
- Has Hypernym weapon
- Has Hypernym cutlery
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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.
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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.
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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
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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
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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{)})"
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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)
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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
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Application of GLTApplication 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 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)
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Application of GLTApplication of GLT
• Merging of senses using GLT
Break
EVENTSTR
QUALIA
E: event
FORMAL: interruptionAGENTIVE: break_act
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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?
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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
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