A systematic comparison of various statistical alignment models
Computational Linguistics
Parameter optimization for machine-learning of word sense disambiguation
Natural Language Engineering
Natural Language Engineering
Two languages are more informative than one
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Word sense disambiguation within a multilingual framework
Word sense disambiguation within a multilingual framework
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
Memory-Based Language Processing (Studies in Natural Language Processing)
Memory-Based Language Processing (Studies in Natural Language Processing)
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
COLEUR and COLSLM: A WSD approach to multilingual lexical substitution, tasks 2 and 3 SemEval 2010
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
UvT-WSD1: A cross-lingual word sense disambiguation system
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
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This paper presents a multilingual classification-based approach to Word Sense Disambiguation that directly incorporates translational evidence from four other languages. The need of a large predefined monolingual sense inventory (such as WordNet) is avoided by taking a language-independent approach where the word senses are derived automatically from word alignments on a parallel corpus. As a consequence, the task is turned into a cross-lingual WSD task, that consists in selecting the contextually correct translation of an ambiguous target word. In order to evaluate the viability of cross-lingual Word Sense Disambiguation, we built five classifiers with English as an input language and translations in the five supported languages (viz. French, Dutch, Italian, Spanish and German) as classification output. The feature vectors incorporate both local context features as well as translation features that are extracted from the aligned translations. The experimental results confirm the validity of our approach: the classifiers that employ translational evidence outperform the classifiers that only exploit local context information. Furthermore, a comparison with state-of-the-art systems for the same task revealed that our system outperforms all other systems for all five target languages.