Semi-supervised Word Sense Disambiguation with Neural Models
Table 8: F1 scores of label propagation
['[BOLD] eval data  [BOLD] model', 'SemCor  [BOLD] train data', 'SemCor  [BOLD] all', 'SemCor  [BOLD] n.', 'SemCor  [BOLD] v.', 'SemCor  [BOLD] adj.', 'MASC  [BOLD] adv.', 'MASC  [BOLD] all', 'MASC  [BOLD] n.', 'MASC  [BOLD] v.', 'MASC  [BOLD] adj.', '[BOLD] adv.']
5.2.3 Semi-supervised WSD We evaluate our semi-supervised WSD classifier in this subsection. We evaluate the performance of the algorithm by comparing the predicted labels and the gold labels on eval nodes. We did see significant improvements, 6.3% increase on SemCor and 7.3% increase on MASC, using LP with the LSTM language model. We hypothesize that this is because LP is sensitive to the quality of the graph distance metric.