Structuring the Lexicon: A Clustered Model for Near-Synonymy by Dagmar Divjak
By Dagmar Divjak
Structuring the Lexicon offers a cognitively real looking, clustered version for near-synonymy that explicitly addresses the query of the way semantic wisdom is shipped alongside the continuum from grammar to lexicon. Usage-based in nature, it proposes an cutting edge method of quantifying which means, whereas re-appraising conventional semantic idea and re-evaluating latest money owed of near-synonymy.
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Extra resources for Structuring the Lexicon: A Clustered Model for Near-Synonymy
Taylor 1998: 184). 1. Reiﬁcation and the argument structure of the ﬁnite verb The ﬁrst diagnostic, henceforth the thing-test, measures the direction of integration between two verbs used in a simplex sentence. It checks whether an inﬁnitive occupies one of the argument structure slots that are opened up by the ﬁnite verb and are typically occupied by a noun: prototypical (subjects and) objects are nominal (Giv´on 1990: 515–517). This diagnostic has been used in ˇ the literature (Suchotin 1960; Cesal 1967; Ickoviˇc 1982), albeit with focus on the interchangeability of inﬁnitive and noun rather than a pronoun.
Absence of referential inclusion The presence of an accusative, genitive, dative, instrumental or prepositional paradigm is no guarantee for the presence of referential inclusion, however. Take the example of probovat’ ‘try’. Although probovat’ ‘try’ can be used in constructions with an accusative slot that refers to nouns (15), this slot does not include reference to inﬁnitives (16). The sentence On (po)proboval pisat’ ‘He ˇ on (po)proboval? ’. tried to write’ is not an answer to Cto Here, a difference in construction goes together with a sharp difference in the meaning of the ﬁnite verb: in dictionaries the probovat’ or ‘try, taste’ from (15) and the probovat’or ‘try’from (16) are listed as separate senses indeed (Apresjan ˇ and Pall 1982; Benson and Benson 1995; Oˇzegov and Svedova 1995 and 1999).
1995 and subsequent publications). Natural Language Processing applications, on the other hand, favor the automatic extraction of near-synonyms based on information included in machine-readable dictionaries of, say, the WordNet type, or on a comparison of distributional patterns extracted from corpus data. These patterns include reference to (a certain number of) words surrounding the target word(s), and/or grammatical dependency relations typical of the target word(s). The latter approach, that is in line with what I propose in this chapter, seems to yield particulary good results.