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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/E17-1010.pdf
- Data
- Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison, Senseval-2