Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison

Alessandro Raganato, Jose Camacho-Collados, Roberto Navigli


Abstract
Word Sense Disambiguation is a long-standing 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 classifier 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.
Anthology ID:
E17-1010
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
99–110
Language:
URL:
https://aclanthology.org/E17-1010
DOI:
Bibkey:
Cite (ACL):
Alessandro Raganato, Jose Camacho-Collados, and Roberto Navigli. 2017. Word Sense Disambiguation: A Unified 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, pages 99–110, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison (Raganato et al., EACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/E17-1010.pdf
Data
Word Sense Disambiguation: a Unified Evaluation Framework and Empirical ComparisonSenseval-2