Redefining the “level” of the “word”

Macpherson, Melissa

doi:10.1007/3-540-55801-2_33

Cited by 2 publications

(1 citation statement)

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“…Selectional restrictions often do not provide all the semantic information that is necessary in NLP systems, however they are at the basis of the majority of computational approaches to syntactic and semantic disambiguation. It has been noticed that representing only the semantics of verbs may be inadequate (Velardi et al 1988;Boguraev 1991;Macpherson 1991). The notion of spreading the semantic load supports the idea that every content word should be represented in the lexicon as the union of all the situations in which it could potentially participate.…”

Section: Introductionmentioning

confidence: 79%

Computational lexicons

Basili

Pazienza

Velardi

1992

Proceedings of the Third Conference on Applied Natural Language Processing -

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When implementing computational lexicons it is important to keep in mind the texts that a NLP system must deal with. Words relate to each other in many different, often queer, ways: this information is rarely found in dictionaries, and it is quite hard to be invented a priori, despite the imagination that linguists exhibit at inventing esoteric examples. In this paper we present the results of an experiment in learning from corpora the frequent selectional restrictions holding between content words. The method is based on the analysis of word associations augmented with syntactic markers and semantic tags. Word pairs are extracted by a morphosyntactic analyzer and clustered according to their semantic tags. A statistical measure is applied to the data to evaluate the significance of a detected relation. Clustered association data render the study of word associations more interesting with several respects: data are more reliable even for smaller corpora, more easy to interpret, and have many practical applications in NLP.

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Section: Introductionmentioning

confidence: 79%