Parameter optimisation for memory-based cross-lingual word-sense disambiguation
Publication year
2013Publisher
New Brunswick, NJ : Association for Computational Linguistics
In
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation, pp. 183-187Related links
Annotation
14 juni 2013
Publication type
Article in monograph or in proceedings

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Organization
Communicatie- en informatiewetenschappen
Former Organization
Bedrijfscommunicatie
Languages used
English (eng)
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
Subject
ADNEXT (Adaptive Information Extraction over Time); Aligned constructions in machine translation; Language & Speech Technology; Language in Society; NederlabAbstract
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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- Academic publications [227881]
- Electronic publications [107344]
- Faculty of Arts [28769]
- Open Access publications [76470]
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