http://www.cnr.it/ontology/cnr/individuo/prodotto/ID172865
SemEval-2010 task 17: All-words word sense disambiguation on a specific domain (Contributo in atti di convegno)
- Type
- Label
- SemEval-2010 task 17: All-words word sense disambiguation on a specific domain (Contributo in atti di convegno) (literal)
- Anno
- 2010-01-01T00:00:00+01:00 (literal)
- Alternative label
Agirre E.; López De Lacalle O.; Fellbaum C.; Hsieh S.; Tesconi M.; Monachini M.; Vossen P.; Vossen P.; Segers R. (2010)
SemEval-2010 task 17: All-words word sense disambiguation on a specific domain
in ACL 2010- SemEval 2010: 5th International Workshop on Semantic Evaluation, Uppsala, Sweden, 15-16 Luglio 2010
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Agirre E.; López De Lacalle O.; Fellbaum C.; Hsieh S.; Tesconi M.; Monachini M.; Vossen P.; Vossen P.; Segers R. (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
- In: ACL 2010 - 5th International Workshop on Semantic Evaluation (Uppsala, Sweden, 15-16 July 2010). Proceedings, pp. 75 - 80. Association for Computational Linguistics, Morristown, NJ, USA ©2010, 2010. In: SemEval-2010 - 5th International Workshop on Semantic Evaluation (Uppsala, Sweden, 15-16 July 2010). Proceedings, pp. 75 - 80. Katrin Erk, Carlo Strapparava (eds.). Association for Computational Linguistics, 2010. (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
- ABSTRACT: Domain portability and adaptation of NLP components and Word Sense Disambiguation systems present new challenges. The difficulties found by supervised systems to adapt might change the way we assess the strengths and weaknesses of supervised and knowledge-based WSD systems. Unfortunately, all existing evaluation datasets for specific domains are lexical-sample corpora. This task presented all-words datasets on the environment domain for WSD in four languages (Chinese, Dutch, English, Italian). 11 teams participated, with supervised and knowledge-based systems, mainly in the English dataset. The results show that in all languages the participants where able to beat the most frequent sense heuristic as estimated from general corpora. The most successful approaches used some sort of supervision in the form of hand-tagged examples from the domain. (literal)
- Note
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- University of the Basque Country, Spain, Princeton University, National Taiwan Normal Univ., CNR-IIT, Pisa, CNR-ILC, Pisa, Vrije Universiteit, Amsterdam (literal)
- Titolo
- SemEval-2010 task 17: All-words word sense disambiguation on a specific domain (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- 978-1-932432-70-1 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- Katrin Erk, Carlo Strapparava (eds.) (literal)
- Abstract
- Domain portability and adaptation of NLP components and Word Sense Disambiguation systems present new challenges. The difficulties found by supervised systems to adapt might change the way we assess the strengths and weaknesses of supervised and knowledge-based WSD systems. Unfortunately, all existing evaluation datasets for specific domains are lexical-sample corpora. This task presented all-words datasets on the environment domain for WSD in four languages (Chinese, Dutch, English, Italian). 11 teams participated, with supervised and knowledge-based systems, mainly in the English dataset. The results show that in all languages the participants where able to beat the most frequent sense heuristic as estimated from general corpora. The most successful approaches used some sort of supervision in the form of hand-tagged examples from the domain. (literal)
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