Semi-supervised Word Sense Disambiguation with Neural Models
Table 2: F1 scores on SemEval all-words tasks. T:SemCor stands for models trained with SemCor. U:OMSTI stands for using OMSTI as unlabeled sentences in semi-supervised WSD. IMS + Word2Vec scores are from [Iacobacci et al.2016]
['[BOLD] model', 'Senseval2  [BOLD] all', 'Senseval2  [BOLD] n.', 'Senseval3  [BOLD] all', 'Senseval3  [BOLD] n.', 'SemEval7  [BOLD] all', 'SemEval7  [BOLD] n.', 'SemEval7-Coarse  [BOLD] all', 'SemEval7-Coarse  [BOLD] n.', 'SemEval13  [BOLD] n.']
Our proposed algorithms achieve the highest all-words F1 scores except for Sem-Eval 2013. Weissenborn et al.\shortciteweissenborn2015multi only disambiguates nouns, and it outperforms our algorithms on Sem-Eval 2013 by 4%, but is ranked behind our algorithms on Senseval-3 and SemEval-7 tasks with an F1 score more than 6% lower than our algorithms.