2015
SemEval-2015 Task 15: A CPA dictionary-entry-building task
BAISA, Vít, Jane BRADBURY, Silvie CINKOVÁ, Ismaïl EL MAAROUF, Adam KILGARRIFF et. al.Základní údaje
Originální název
SemEval-2015 Task 15: A CPA dictionary-entry-building task
Autoři
BAISA, Vít (203 Česká republika, domácí), Jane BRADBURY (826 Velká Británie a Severní Irsko), Silvie CINKOVÁ (203 Česká republika), Ismaïl EL MAAROUF (250 Francie, garant), Adam KILGARRIFF (826 Velká Británie a Severní Irsko) a Octavian POPESCU (642 Rumunsko)
Vydání
Denver, Colorado, Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), od s. 315-324, 10 s. 2015
Nakladatel
Association for Computational Linguistics
Další údaje
Jazyk
angličtina
Typ výsledku
Stať ve sborníku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Forma vydání
elektronická verze "online"
Odkazy
Kód RIV
RIV/00216224:14330/15:00083584
Organizační jednotka
Fakulta informatiky
ISBN
978-1-941643-40-2
Klíčová slova anglicky
semeval; corpus pattern analysis; concordance clustering; semantic evaluation
Štítky
Změněno: 11. 5. 2017 07:43, Mgr. et Mgr. Vít Baisa, Ph.D.
Anotace
V originále
This paper describes the first SemEval task to explore the use of Natural Language Processing systems for building dictionary entries, in the framework of Corpus Pattern Analysis. CPA is a corpus-driven technique which provides tools and resources to identify and represent unambiguously the main semantic patterns in which words are used. Task 15 draws on the Pattern Dictionary of English Verbs (www.pdev.org.uk), for the targeted lexical entries, and on the British National Corpus for the input text. Dictionary entry building is split into three subtasks which all start from the same concordance sample: 1) CPA parsing, where arguments and their syntactic and semantic categories have to be identified, 2) CPA clustering, in which sentences with similar patterns have to be clustered and 3) CPA automatic lexicography where the structure of patterns have to be constructed automatically. Subtask 1 attracted 3 teams, though none could beat the baseline (rule-based system). Subtask 2 attracted 2 teams, one of which beat the baseline (majority-class classifier). Subtask 3 did not attract any participant. The task has produced a major semantic multidataset resource which includes data for 121 verbs and about 17,000 annotated sentences, and which is freely accessible.
Návaznosti
LM2010013, projekt VaV |
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7F14047, projekt VaV |
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