Word Sense Disambiguation is a longstanding task in Natural Language Processing, lying at the core of human language understanding. However, the evaluation of automatic systems has been problematic, mainly due to the lack of a reliable evaluation framework. In this paper we develop a unified evaluation framework and analyze the performance of various Word Sense Disambiguation systems in a fair setup. The results show that supervised systems clearly outperform knowledge-based models. Among the supervised systems, a linear classi- fier trained on conventional local features still proves to be a hard baseline to beat. Nonetheless, recent approaches exploiting neural networks on unlabeled corpora achieve promising results, surpassing this hard baseline in most test sets.

Raganato, A., Camacho-collados, J., Navigli, R. (2017). Word sense disambiguation: a uinified evaluation framework and empirical comparison. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers (pp.99-110). Association for Computational Linguistics [10.18653/v1/e17-1010].

Word sense disambiguation: a uinified evaluation framework and empirical comparison

Raganato, Alessandro
;
2017

Abstract

Word Sense Disambiguation is a longstanding task in Natural Language Processing, lying at the core of human language understanding. However, the evaluation of automatic systems has been problematic, mainly due to the lack of a reliable evaluation framework. In this paper we develop a unified evaluation framework and analyze the performance of various Word Sense Disambiguation systems in a fair setup. The results show that supervised systems clearly outperform knowledge-based models. Among the supervised systems, a linear classi- fier trained on conventional local features still proves to be a hard baseline to beat. Nonetheless, recent approaches exploiting neural networks on unlabeled corpora achieve promising results, surpassing this hard baseline in most test sets.
No
paper
Semantics; Knowledge based systems; disambiguation WSD
English
15th Conference of the European Chapter of the Association for Computational Linguistics
978-1-945626-34-0
http://aclweb.org/anthology/E/E17/E17-1010.pdf
Raganato, A., Camacho-collados, J., Navigli, R. (2017). Word sense disambiguation: a uinified evaluation framework and empirical comparison. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers (pp.99-110). Association for Computational Linguistics [10.18653/v1/e17-1010].
Raganato, A; Camacho-collados, J; Navigli, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/361554
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