Identifying, Finding and Encoding Semantic Relations Christiane - - PDF document

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Identifying, Finding and Encoding Semantic Relations

Christiane Fellbaum Princeton University

Questions

• What kind of semantic knowledge does the

NLP community need?

• How to represent semantic knowledge?
• How to expand on our present knowledge?

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Assumptions

Need a repository for word form-meaning pairs (lexicon) that serves as a standard for word sense representation, applications and evaluation

Assumptions

• Lexicon has a structure
• Entities, events, properties are labeled (more or

less) systematically

• Structure and lexicalization patterns can be

captured with semantic and lexical relations

• Relations reflect (dis)similarities among labeled

concepts in a fairly systematic way

• Semantic similarity as reflected by relations is

useful for WSD

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WordNet--the Plus side

• Broad coverage
• Multilingual
• Freely accessible
• Continually enriched both by Princeton and the

user community

Limitations of WordNet

• Too sparse: too few relations, too few links
• Few syntagmatic (cross-POS) links
• Links are not weighted
• Many arcs are not directed

(dollar->green, ?green->dollar)

• Sense inventory is too fine-grained for current

automatic WSD

• Polysemy would be less of a problem if

WordNet’s internal connectivity was greater

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Sources of WordNet-style relations

• Classical relations (Aristotle)
• Lexical-semantic analysis of entities, events

(causation, entailment, troponymy,...)

• Finding examples via lexico-syntactic patterns

(Cruse 1986; Hearst 1993)

• Lexico-syntactic patterns presuppose specific

relations

WordNet connections based on human judgment

• (Robust) word association norms
• Human judgments of associations among

WordNet concepts show many connections not currently encoded (WordNetPlus, Boyd- Graber et al. 2006)

• Can’t all be easily classified or labeled!

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Moving away from preconceived relations

• Reconsider: structure of the lexicon
• Which concepts are distinguished and

labeled with words?

• Discover systematic differences among

concepts/words that can be encoded as relations

Focus: Rigidity

• Important meta-property for distinguishing

concepts in ontology

• Rigidity distinguishes Types vs. Roles

e.g., DOLCE ontology (Guarino and Welty 2002), Generative Lexicon (Pustejovsky 1995)

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Rigidity

rigid entities: dog, orchid, man, shirts,.. vs. non-rigid: pet, houseplant, teacher, laundry,...

Adjectives

stage-level vs. individual-level (Carlson 1977) tall, intelligent, female,... vs. married, tired, surprised, ... time-dependent: John is no longer tired/married/*tall/*intelligent

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Rigid and non-rigid terms may be related via shared hypernym plant

• rchid violet *houseplant

Rigid and non-rigid terms are compatible and not mutually exclusive: This is an orchid and a houseplant (type, role co-hyponyms) cf. *This is an orchid and a violet (type co-hyponyms)

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Non-rigid properties are defeasible: This orchid is not a houseplant (type) *This orchid is not a plant (role)

Type and role nouns noun are semantically similar when sharing a superordinate A given entity can be labeled with both kinds of nouns Useful for co-reference resolution Temporal relations

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Encoding

Role and type nouns can be systematically distinguished and encoded linked to shared superordinates with para(llel) relations (Cruse 1986): plant

• rchid houseplant

Relations in the verb lexicon

Lexicalization patterns show systematic, productive encoding of hyponymy (troponymy), causation Verb classes: Manner verbs Change-of-state verbs Can be distinguished via syntactic criteria

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Another relation

Analogous to Type-Role distinction Distinguish hyponyms (troponyms) from “purpose” verbs examples: exercise, control, greet, help, punish don’t encode manner or change-of state not productive (?)

“purpose” verbs

move exercise run running is necessarily a kind of moving (hyponym) running is not necessarily a kind of exercising

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Co-Hyponyms defeasible/non-defeasible

Run but not {exercise/*move} Wave but not {greet/*gesture} Scrub but not {clean/*rub} the table Fair amount of verb hyponyms in WN are defeasible But no systematic encoding, distinction Relation is not always captured by co-hyponymy

• Find verbs expressing purpose
• encode them in WordNet with “parallel”

relation, following Cruse’s suggestion for Types and Role

• Problem: such concepts are not

systematically encoded

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Finding examples via lexico- syntactic patterns

V-ing is (not) V-ing to V is (not) to V V-ing is a way of V-ing Patterns are also valid for regular hyponyms but few such pairs are extracted (for pragmatic reasons?)

Web examples

spraying the action with a little WD-40 is not cleaning shake hands, using the right hand, and explain that his is a way of greeting one another tipping, leaving a gratuity, is a way of thanking people for their service

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Boyd-Graber, J., Fellbaum, C., Osherson, D. and Schapire, R. (2006). Adding dense, weighted connections to WordNet. Proceedings of the 3rd Global WordNet Association, Jeju, Korea. Carlson, G. (1977). Reference to Kinds in English. University

• f Massachusetts, Amherst Dissertation.

Cruse, Alan (1986). Lexical Semantics. Cambridge: Cambride University Press. Fellbaum, Christiane (Ed., 1998). WordNet. Cambridge, MA: MIT Press. Fellbaum, Christiane (2002a). Parallel hierachies in the verb

• lexicon. in: K. Simov, (Ed.), Proceedings of the Ontolex02

Workshop, LREC, Las Palmas, Spain.

. Fellbaum, Christiane (2002b). On the semantics of

• troponymy. In: Green, R. et al. (Eds.) Relations.

Dordrecht: Kluwer. Fellbaum, C. (2003). Distinguishing Verb Types in a Lexical Ontology. Proceedings of the Conference on the Generative Lexicon, Geneva, Switzerland Guarino, N. and Welty, C. (2002), Evaluating Ontological Decisions with OntoClean. Communications of the ACM 45:2, 61-65. Hearst, M. (1992). Automatic acquisition of hyponyms from large text corpora. COLING 92, 539-545. Pustejovsky, J. (1995). The Generative Lexicon. Cambridge, MA: MIT Press.