Computational Linguistics - Special issue on web as corpus
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
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Building a sense tagged corpus with open mind word expert
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Scaling up word sense disambiguation via parallel texts
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
SemEval-2010 task 2: cross-lingual lexical substitution
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
SemEval-2010 task 2: Cross-lingual lexical substitution
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Measuring similarity of word meaning in context with lexical substitutes and translations
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Unsupervised cross-lingual lexical substitution
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Correcting semantic collocation errors with L1-induced paraphrases
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The cross-lingual lexical substitution task
Language Resources and Evaluation
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We made use of parallel texts to gather training and test examples for the English lexical sample task. Two tracks were organized for our task. The first track used examples gathered from an LDC corpus, while the second track used examples gathered from a Web corpus. In this paper, we describe the process of gathering examples from the parallel corpora, the differences with similar tasks in previous SENSEVAL evaluations, and present the results of participating systems.