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Title: Parameter optimisation for memory-based cross-lingual word-sense disambiguation
Author(s): Gompel, M. van ; Bosch, A.P.J. van den
Publication year: 2013
In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation, pp. 183-187
Publisher: New Brunswick, NJ : Association for Computational Linguistics
Related links: http://aclweb.org/anthology/S/S13/S13-2033.pdf
Annotation: 14 juni 2013
Publication type: Article in monograph or in proceedings

Please use this identifier to cite or link to this item : https://hdl.handle.net/2066/112947   https://hdl.handle.net/2066/112947

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Subject: ADNEXT (Adaptive Information Extraction over Time)
Aligned constructions in machine translation
Language & Speech Technology
Language in Society
Nederlab
Nederlab
Organization: Communicatie- en informatiewetenschappen
Former Organization: Bedrijfscommunicatie
Book title: Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation
Page start: p. 183
Page end: p. 187
Abstract:
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.
Languages used: English (eng)

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