Using BERT for Word Sense Disambiguation
Table 2: F1-score(%) for the English all-word WSD evaluation. We report the F1-score of these systems on the four datasets (SE2, SE3, SE13, and SE15), on every part-of-speech type of polyseme (Noun, Verb, Adj, and Adv), and on the overall test dataset.
['System', 'Test Datasets SE2', 'Test Datasets SE3', 'Test Datasets SE13', 'Test Datasets SE15', 'Concatenation of Test Datasets Noun', 'Concatenation of Test Datasets Verb', 'Concatenation of Test Datasets Adj', 'Concatenation of Test Datasets Adv', 'All']
The Bert and Bertdef in the table use the average operation to merge hidden states. They don’t use the sentence vectors. Our best model improves the state-of-the-art results by 5.2%, which indicates that BERT encoder is quite powerful in the WSD task. Moreover, our models outperform previous models on all of the four datasets and PoS types. Introducing sense definitions can significantly improve the performance of the Bert model. The F1-score of Bertdef is better than Bert on almost all datasets and PoS types except on the adverbs, which reveals the efficiency of introducing sense definitions.