Unsupervised Word Sense Disambiguation Rivaling Supervised Methods
Yarowsky, 1995, Unsupervised Word Sense Disambiguation Rivaling Supervised Methods (Proceedings of the 33rd Annual Meeting of ACL)
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8 Evaluation
- How did Yarowsky’s unsupervised algorithm compare with supervised training and with Schütze’s method?It nearly matched a supervised decision list trained on identical hand-tagged contexts (95.5% vs 96.1%), exceeded it with one sense per discourse (96.5%), and beat Schütze’s unsupervised algorithm on the same 4 words (96.7% vs 92.2%).
- Yarowsky, 1995, Unsupervised Word Sense Disambiguation Rivaling Supervised Methods (Proceedings of the 33rd Annual Meeting of ACL), §8
- How much did the one-sense-per-discourse constraint reduce errors?Applied after convergence, it cut the error rate by about 27% on average; applied at each iteration, it gave the best accuracy.
- Yarowsky, 1995, Unsupervised Word Sense Disambiguation Rivaling Supervised Methods (Proceedings of the 33rd Annual Meeting of ACL), §8
Source
- David Yarowsky. 1995. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pp. 189–196. https://aclanthology.org/P95-1026/