Exploiting parallel texts for word sense disambiguation: an empirical study
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Modeling consensus: classifier combination for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
SemEval-2010 task 2: cross-lingual lexical substitution
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
Pattern learning and active feature selection for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
The Johns Hopkins SENSEVAL2 system descriptions
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
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Word Sense Disambiguation (WSD) is considered one of the most important problems in Natural Language Processing. Even if the problem of WSD is difficult, when we consider its bilingual version, this problem becomes to be much more complex. In this case, it is needed not only to find the correct translation, but this translation must consider the contextual senses of the original sentence (in a source language), in order to find the correct sense (in the target language) of the source word. In this paper we propose a model based on n-grams (3-grams and 5-grams) that significantly outperforms the last results that we presented at the cross-lingual word sense disambiguation task at the SemEval-2 forum. We use a naïve Bayes classifier for determining the probability of a target sense (in a target language) given a sentence which contains the ambiguous word (in a source language). For this purpose, we use a bilingual statistical dictionary, which is calculated with Giza++ by using the EUROPARL parallel corpus, in order to determine the probability of a source word to be translated to a target word (which is assumed to be the correct sense of the source word but in a different language). As we mentioned, the results were compared with those of an international competition, obtaining a good performance.