Unsupervised cross-lingual lexical substitution

Authors:
Marianna Apidianaki
Affiliations:
Alpage, INRIA & Univ Paris Diderot, Sorbonne Paris Citéé, Paris, France
Venue:
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Year:
2011

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Abstract

Cross-Lingual Lexical Substitution (CLLS) is the task that aims at providing for a target word in context, several alternative substitute words in another language. The proposed sets of translations may come from external resources or be extracted from textual data. In this paper, we apply for the first time an unsupervised cross-lingual WSD method to this task. The method exploits the results of a cross-lingual word sense induction method that identifies the senses of words by clustering their translations according to their semantic similarity. We evaluate the impact of using clustering information for CLLS by applying the WSD method to the SemEval-2010 CLLS data set. Our system performs better on the 'out-of-ten' measure than the systems that participated in the SemEval task, and is ranked medium on the other measures. We analyze the results of this evaluation and discuss avenues for a better overall integration of unsupervised sense clustering in this setting.