By Patrick Saint-Dizier, Evelyn Viegas
Computational Lexical Semantics is without doubt one of the first volumes to supply types for the production of assorted types of automatic lexicons for the automated therapy of common language, with purposes to, between different issues, laptop translation, computerized indexing, database front-ends, and information extraction. It specializes in semantic concerns, as obvious via linguists, psychologists, and laptop scientists. along with describing educational study, the publication additionally covers ongoing business tasks.
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Extra resources for Computational Lexical Semantics (Studies in Natural Language Processing)
Within the taxonomy of items of cutlery, knife shows all the signs of being a basic level item: it is used for neutral reference, and represents the highest taxonomic level with which distinct behavior patterns and visual images can be associated. Fourth, the superordinate sense is marked and appears only under contextual pressure; the unmarked readings of knife are specific ones, but they vary from domain to domain. In a neutral context, the dominant reading of teacher is sexneutral: You would do better with a teacher, rather than trying to learn on your own; knife, on the other hand, like bank (only less so) is somewhat uneasy in a neutral context: / want you to go out and buy a knife.
Cruse say, more than one meaning definition is required to cover the full range of their uses. This feature correlates well with the lack of a superordinate use, but not perfectly, because (a) some words are 'autohyponymous', that is, one of their senses is hyponymous to the other, and (b) some monosemic words require a disjunct definition of the form 'A or B \ It is arguable that, for instance, princess is of the latter type, since separate definitions are required for princesses 'of the blood' and princesses by marriage.
5. 6. 7. semantic representations (SemR) deep syntactic representations (DSyntR) surface syntactic representations (SSyntR) deep morphological representations (DMorphR) surface morphological representations (SMorphR) deep phonetic representations (DPhonR) surface phonetic representations (SPhonR) We are interested here in the first three levels of description: SemR is represented as a semantic network, DSyntR and SSyntR as trees. The syntactic levels use dependency trees: the grammatical relations are explicitly labeled on the branches, whereas lexemes and grammatical words are labeled on the nodes.
Computational Lexical Semantics (Studies in Natural Language Processing) by Patrick Saint-Dizier, Evelyn Viegas