TAILIEUCHUNG - Báo cáo khoa học: "An Empirical Study on Class-based Word Sense Disambiguation"

As empirically demonstrated by the last SensEval exercises, assigning the appropriate meaning to words in context has resisted all attempts to be successfully addressed. One possible reason could be the use of inappropriate set of meanings. In fact, WordNet has been used as a de-facto standard repository of meanings. However, to our knowledge, the meanings represented by WordNet have been only used for WSD at a very fine-grained sense level or at a very coarse-grained class level. We suspect that selecting the appropriate level of abstraction could be on between both levels. . | An Empirical Study on Class-based Word Sense Disambiguation Ruben Izquierdo Armando Suarez Deparment of Software and Computing Systems University of Alicante. Spain ruben armando @ German Rigau IXA NLP Group. EHU. Donostia Spain Abstract As empirically demonstrated by the last SensEval exercises assigning the appropriate meaning to words in context has resisted all attempts to be successfully addressed. One possible reason could be the use of inappropriate set of meanings. In fact WordNet has been used as a de-facto standard repository of meanings. However to our knowledge the meanings represented by WordNet have been only used for WSD at a very fine-grained sense level or at a very coarse-grained class level. We suspect that selecting the appropriate level of abstraction could be on between both levels. We use a very simple method for deriving a small set of appropriate meanings using basic structural properties of WordNet. We also empirically demonstrate that this automatically derived set of meanings groups senses into an adequate level of abstraction in order to perform class-based Word Sense Disambiguation allowing accuracy figures over 80 . 1 Introduction Word Sense Disambiguation WSD is an intermediate Natural Language Processing NLP task which consists in assigning the correct semantic interpretation to ambiguous words in context. One of the most successful approaches in the last years is the supervised learning from examples in which statistical or Machine Learning classification models are induced from semantically annotated corpora Marquez et al. 2006 . Generally supervised systems have obtained better results than the unsupervised ones as shown by experimental work and international evaluation exercises such This paper has been supported by the European Union under the projects QALL-ME FP6 IST-033860 and KYOTO FP7 IcT-211423 and the Spanish Government under the project Text-Mess TIN2006-15265-C06-01 and KNOW .

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