KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation
Table 2: F1-scores (%) for English all-words fine-grained WSD with the utilization of manually labeled training data
['Methods', 'Test Datasets SE2', 'Test Datasets SE3', 'Test Datasets SE13', 'Test Datasets SE15', 'Concatenation of All Test Sets Nouns', 'Concatenation of All Test Sets Verbs', 'Concatenation of All Test Sets Adj.', 'Concatenation of All Test Sets Adv.', 'Concatenation of All Test Sets All']
From the comparison between MLab and MLab+ULab setting, it shows that unsupervised learning improves the generalization performance. From the comparison between MLab+ULab and MLab+DisDict+ULab setting, it illustrates the knowledge in DisDict has a good quality and the combination of knowledge and unlabeled data really generates reliable sense-labeled data, thus improves the overall performance. From the comparison between MLab+DisDict+ULab and MLab+DisDict+ULab+MFS setting, it shows that MFS has a good complementarity to supervised WSD.