Parameter optimisation for memory-based cross-lingual word-sense disambiguation
New Brunswick, NJ : Association for Computational Linguistics
InSecond Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation, pp. 183-187
14 juni 2013
Article in monograph or in proceedings
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Communicatie- en informatiewetenschappen
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation
SubjectADNEXT (Adaptive Information Extraction over Time); Aligned constructions in machine translation; Language & Speech Technology; Language in Society; Nederlab; Nederlab
We present our system WSD2 which participated in the Cross-Lingual Word-Sense Disambiguation task for SemEval 2013 (Lefever and Hoste, 2013). The system closely resembles our winning system for the same task in SemEval 2010. It is based on k-nearest neighbour classiﬁers which map words with local and global context features onto their translation, i.e. their cross-lingual sense. The system participated in the task for all ﬁve languages and obtained winning scores for four of them when asked to predict the best translation(s). We tested various conﬁgurations of our system, focusing on various levels of hyperparameter optimisation and feature selection. Our ﬁnal results indicate that hyperparameter optimisation did not lead to the best results, indicating overﬁtting by our optimisation method in this aspect. Feature selection does have a modest positive impact.
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