SciELO - Scientific Electronic Library Online

 
vol.22 issue1New Similarity Function for Scientific Articles Clustering based on the Bibliographic ReferencesAutomatic Theorem Proving for Natural Logic: A Case Study on Textual Entailment author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

Print version ISSN 1405-5546

Abstract

GELBUKH, Alexander. Inferences for Enrichment of Collocation Databases by Means of Semantic Relations. Comp. y Sist. [online]. 2018, vol.22, n.1, pp.103-117. ISSN 1405-5546.  http://dx.doi.org/10.13053/cys-22-1-2923.

A text consists of words that are syntactically linked and semantically combinable—like “political party,” “pay attention,” or “stone cold.” Such semantically plausible combinations of two content words, which we hereafter refer to as collocations, are important knowledge in many areas of computational linguistics. We present the structure of a lexical resource that provides such knowledge—a collocation database (CBD). Since such databases cannot be complete under any reasonable compilation procedure, we consider heuristic-based inference mechanisms that predict new plausible collocations based on the ones present in the CDB, with the help of a WordNet-like thesaurus: if an available collocation combines the entries A and B, and B is ‘similar’ to C, then A and C are supposed to constitute a collocation of the same category. Also, we describe the semantically induced morphological categories suiting for such inference, as well as the heuristics for filtering out wrong hypotheses. We discuss the experience in inferences obtained with CrossLexica CDB.

Keywords : Collocations; inference rules; enrichment; synonyms; hypernyms; meronyms.

        · text in English     · English ( pdf )