D
2016
VPS-GradeUp: Graded Decisions on Usage Patterns
BAISA, Vít, Silvie CINKOVA, Ema KREJČOVÁ and Anna VERNEROVÁ
Basic information
Original name
VPS-GradeUp: Graded Decisions on Usage Patterns
Authors
BAISA, Vít (203 Czech Republic, belonging to the institution), Silvie CINKOVA (203 Czech Republic), Ema KREJČOVÁ (203 Czech Republic) and Anna VERNEROVÁ (203 Czech Republic)
Edition
Portorož, Slovenia, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), p. 823-827, 5 pp. 2016
Publisher
European Language Resources Association (ELRA)
Other information
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Slovenia
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
electronic version available online
RIV identification code
RIV/00216224:14330/16:00090124
Organization unit
Faculty of Informatics
Keywords in English
Corpus Creation; Corpus Annotation; Word Sense Disambiguation; Validation of Language Resources
Tags
International impact, Reviewed
V originále
We present VPS-GradeUp - a set of 11,400 graded human decisions on usage patterns of 29 English lexical verbs from the Pattern Dictionary of English Verbs by Patrick Hanks. The annotation contains, for each verb lemma, a batch of 50 concordances with the given lemma as KWIC, and for each of these concordances we provide a graded human decision on how well the individual PDEV patterns for this particular lemma illustrate the given concordance, indicated on a 7-point Likert scale for each PDEV pattern. With our annotation, we were pursuing a pilot investigation of the foundations of human clustering and disambiguation decisions with respect to usage patterns of verbs in context. The data set is publicly available at http://hdl.handle.net/11234/1-1585.
Links
LM2015071, research and development project | Name: Jazyková výzkumná infrastruktura v České republice (Acronym: LINDAT-Clarin) | Investor: Ministry of Education, Youth and Sports of the CR |
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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 |
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