Detailed Information on Publication Record
2016
Finding Definitions in Large Corpora with Sketch Engine
KOVÁŘ, Vojtěch, Monika MOČIARIKOVÁ and Pavel RYCHLÝBasic 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 |
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7F14047, research and development project |
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