Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation

EMNLP 2017 Alexander PanchenkoFide MartenEugen RuppertStefano FaralliDmitry UstalovSimone Paolo PonzettoChris Biemann

Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images... (read more)

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