Two Knowledge-based Methods for High-Performance Sense Distribution Learning

Authors

  • Tommaso Pasini Sapienza University of Rome
  • Roberto Navigli Sapienza University of Rome

Keywords:

Word sense disambiguation, sense distribution learning, most frequent sense, wsd, nlp

Abstract

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 language-independent methods for computing the distribution of senses given a raw corpus of sentences. Intrinsic and extrinsic evaluations show that our methods 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. Our sense distributions are available at http://trainomatic.org.

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Published

2018-04-27

How to Cite

Pasini, T., & Navigli, R. (2018). Two Knowledge-based Methods for High-Performance Sense Distribution Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11961