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Description
  • Many recent NLP applications, including machine translation and information retrieval, could benefit from semantic analysis of language data on the sentence level. This paper presents a method for automatic disambiguation of verb valency frames on Czech data. For each verb occurrence, we extracted features describing its local context. We experimented with diverse types of features, including morphological, syntax-based, idiomatic, animacy and WordNet-based features. The main contribution of the paper lies in determining which ones are most useful for the disambiguation task. The considered features were classified using decision trees, rule-based learning and a Naive Bayes classifier. We evaluated the methods using 10-fold cross-validation on VALEVAL, a manually annotated corpus of frame annotations containing 7,778 sentences. Syntax-based features have shown to be the most effective. When we used the full set of features, we achieved an accuracy of 80.55% against the baseline 67.87% obtained by assi
  • Many recent NLP applications, including machine translation and information retrieval, could benefit from semantic analysis of language data on the sentence level. This paper presents a method for automatic disambiguation of verb valency frames on Czech data. For each verb occurrence, we extracted features describing its local context. We experimented with diverse types of features, including morphological, syntax-based, idiomatic, animacy and WordNet-based features. The main contribution of the paper lies in determining which ones are most useful for the disambiguation task. The considered features were classified using decision trees, rule-based learning and a Naive Bayes classifier. We evaluated the methods using 10-fold cross-validation on VALEVAL, a manually annotated corpus of frame annotations containing 7,778 sentences. Syntax-based features have shown to be the most effective. When we used the full set of features, we achieved an accuracy of 80.55% against the baseline 67.87% obtained by assi (en)
Title
  • On Automatic Assignment of Verb Valency Frames in Czech
  • On Automatic Assignment of Verb Valency Frames in Czech (en)
skos:prefLabel
  • On Automatic Assignment of Verb Valency Frames in Czech
  • On Automatic Assignment of Verb Valency Frames in Czech (en)
skos:notation
  • RIV/00216208:11320/06:10077923!RIV11-GA0-11320___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET101470416), P(GA405/06/0589), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 490274
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/06:10077923
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • czech; frames; valency; verb; assignment; automatic (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [85AF28989376]
http://linked.open...v/mistoKonaniAkce
  • Genova, Italy
http://linked.open...i/riv/mistoVydani
  • Genova, Italy
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC 2006)
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Semecký, Jiří
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • ELRA
https://schema.org/isbn
  • 2-9517408-2-4
http://localhost/t...ganizacniJednotka
  • 11320
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