KILGARRIFF, Adam, Miloš JAKUBÍČEK, Vojtěch KOVÁŘ, Pavel RYCHLÝ and Vít SUCHOMEL. Finding Terms in Corpora for Many Languages with the Sketch Engine. Online. In Proceedings of the Demonstrations at the 14th Conferencethe European Chapter of the Association for Computational Linguistics. Gothenburg, Sweden: The Association for Computational Linguistics, 2014, p. 53-56. ISBN 978-1-937284-75-6.
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Basic information
Original name Finding Terms in Corpora for Many Languages with the Sketch Engine
Authors KILGARRIFF, Adam (826 United Kingdom of Great Britain and Northern Ireland), Miloš JAKUBÍČEK (203 Czech Republic, guarantor, belonging to the institution), Vojtěch KOVÁŘ (203 Czech Republic, belonging to the institution), Pavel RYCHLÝ (203 Czech Republic, belonging to the institution) and Vít SUCHOMEL (203 Czech Republic, belonging to the institution).
Edition Gothenburg, Sweden, Proceedings of the Demonstrations at the 14th Conferencethe European Chapter of the Association for Computational Linguistics, p. 53-56, 4 pp. 2014.
Publisher The Association for Computational Linguistics
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW Plný text výsledku
RIV identification code RIV/00216224:14330/14:00075387
Organization unit Faculty of Informatics
ISBN 978-1-937284-75-6
Keywords in English terminology; terms; corpora; sketch engine
Tags best
Tags International impact, Reviewed
Changed by Changed by: RNDr. Vít Suchomel, Ph.D., učo 139723. Changed: 29/10/2014 09:19.
Abstract
Term candidates for a domain, in a language, can be found by • taking a corpus for the domain, and a refer- ence corpus for the language • identifying the grammatical shape of a term in the language • tokenising, lemmatising and POS-tagging both corpora • identifying (and counting) the items in each corpus which match the grammatical shape • for each item in the domain corpus, compar- ing its frequency with its frequency in the refence corpus. Then, the items with the highest frequency in the domain corpus in comparison to the reference cor- pus will be the top term candidates. None of the steps above are unusual or innova- tive for NLP (see, e. g., (Aker et al., 2013), (Go- jun et al., 2012)). However it is far from trivial to implement them all, for numerous languages, in an environment that makes it easy for non- programmers to find the terms in a domain. This is what we have done in the Sketch Engine (Kilgarriff et al., 2004), and will demonstrate. In this abstract we describe how we addressed each of the stages above.
Links
LM2010013, research and development projectName: LINDAT-CLARIN: Institut pro analýzu, zpracování a distribuci lingvistických dat (Acronym: LINDAT-Clarin)
Investor: Ministry of Education, Youth and Sports of the CR
MUNI/A/0765/2013, interní kód MUName: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Acronym: SKOMU)
Investor: Masaryk University, Category A
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