Finding Terms in Corpora for Many Languages with the Sketch Engine

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F14%3A00075387" target="_blank" >RIV/00216224:14330/14:00075387 - isvavai.cz</a>

Výsledek na webu

<a href="http://aclweb.org/anthology/E/E14/E14-2014.pdf" target="_blank" >http://aclweb.org/anthology/E/E14/E14-2014.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Finding Terms in Corpora for Many Languages with the Sketch Engine

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Finding Terms in Corpora for Many Languages with the Sketch Engine

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/LM2010013" target="_blank" >LM2010013: LINDAT-CLARIN: Institut pro analýzu, zpracování a distribuci lingvistických dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Demonstrations at the 14th Conferencethe European Chapter of the Association for Computational Linguistics

ISBN

9781937284756

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

53-56

Název nakladatele

The Association for Computational Linguistics

Místo vydání

Gothenburg, Sweden

Místo konání akce

Gothenburg, Sweden

Datum konání akce

1. 1. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—