D 2016

Finding Definitions in Large Corpora with Sketch Engine

KOVÁŘ, Vojtěch, Monika MOČIARIKOVÁ and Pavel RYCHLÝ

Basic information

Original name

Finding Definitions in Large Corpora with Sketch Engine

Authors

KOVÁŘ, Vojtěch (203 Czech Republic, guarantor, belonging to the institution), Monika MOČIARIKOVÁ (703 Slovakia, belonging to the institution) and Pavel RYCHLÝ (203 Czech Republic, belonging to the institution)

Edition

Portorož, Slovenia, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), p. 391-394, 4 pp. 2016

Publisher

European Language Resources Association (ELRA)

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

France

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

storage medium (CD, DVD, flash disk)

RIV identification code

RIV/00216224:14330/16:00088334

Organization unit

Faculty of Informatics

ISBN

978-2-9517408-9-1

Keywords in English

Sketch Engine; definition; definitions; CQL; corpora

Tags

Změněno: 20/12/2016 13:55, doc. Mgr. Pavel Rychlý, Ph.D.

Abstract

V originále

The paper describes automatic definition finding implemented within the leading corpus query and management tool, Sketch Engine. The implementation exploits complex pattern-matching queries in the corpus query language (CQL) and the indexing mechanism of word sketches for finding and storing definition candidates throughout the corpus. The approach is evaluated for Czech and English corpora, showing that the results are usable in practice: precision of the tool ranges between 30 and 75 percent (depending on the major corpus text types) and we were able to extract nearly 2 million definition candidates from an English corpus with 1.4 billion words. The feature is embedded into the interface as a concordance filter, so that users can search for definitions of any query to the corpus, including very specific multi-word queries. The results also indicate that ordinary texts (unlike explanatory texts) contain rather low number of definitions, which is perhaps the most important problem with automatic definition finding in general.

Links

GA15-13277S, research and development project
Name: Hyperintensionální logika pro analýzu přirozeného jazyka
Investor: Czech Science Foundation
7F14047, research and development project
Name: Harvesting big text data for under-resourced languages (Acronym: HaBiT)
Investor: Ministry of Education, Youth and Sports of the CR