Interpretability in Word Sense Disambiguation using Tsetlin Machine
Word Sense Disambiguation (WSD) is a longstanding unresolved task in Natural Language Processing. The challenge lies in the fact that words with the same spelling can have completely different senses, sometimes depending on subtle characteristics of the context. A weakness of the state-of-the-art supervised models, however, is that it can be difficult to interpret them, making it harder to check if they capture senses accurately or not. In this paper, we introduce a novel Tsetlin Machine (TM) based supervised model that distinguishes word senses by means of conjunctive clauses. The clauses are formulated based on contextual cues, represented in propositional logic. Our experiments on CoarseWSD-balanced dataset indicate that the learned word senses can be relatively effortlessly interpreted by analyzing the converged model of the TM. Additionally, the classification accuracy is higher than that of FastText-Base and similar to that of FastText-CommonCrawl.
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