Knowledge-based Word Sense Disambiguation using Topic Models
Table 2: Comparison of F1 scores on different POS tags over all datasets. WSD-TM corresponds to the proposed method. The best results in each column among knowledge-based systems are marked in bold.
['[EMPTY]', '[BOLD] System', '[BOLD] Nouns', '[BOLD] Verbs', '[BOLD] Adjectives', '[BOLD] Adverbs', '[BOLD] All']
The proposed method, denoted by WSD-TM in the tables referring to WSD using topic models, outperforms the state-of-the-art WSD system by a significant margin (p-value <0.01) by achieving an overall F1-score of 66.9 as compared to Moro14’s score of 65.5. We also observe that the performance of the proposed model is not much worse than the best supervised system, Melamud16 (69.4). The proposed system outperforms all previous knowledge-based systems over all parts of speech. This indicates that using document context helps in disambiguating words of all PoS tags.