Knowing the correct distribution of senses within a corpus can potentially boost the performance of Word Sense Disambiguation (WSD) systems by many points. We present two fully automatic and languageindependent methods for computing the distribution of senses given a raw corpus of sentences. Intrinsic and extrinsic evaluations show that our meth- ods outperform the current state of the art in sense distribution learning and the strongest baselines for the most frequent sense in multiple languages and on domain-specific test sets.
Tommaso Pasini and Roberto Navigli
Two Knowledge-based Methods for High-Performance Sense Distribution Learning
Proceedings of the AAAI Conference on Artificial Intelligence 2018, New Orleans, Louisiana, United States, 4-7 February 2018.