Leveraging Lexical Substitutes for Unsupervised Word Sense Induction


  • Domagoj Alagić University of Zagreb
  • Jan Šnajder University of Zagreb
  • Sebastian Padó University of Stuttgart, Institut für Maschinelle Sprachverarbeitung




word sense induction, lexical substitution


Word sense induction is the most prominent unsupervised approach to lexical disambiguation. It clusters word instances, typically represented by their bag-of-words contexts. Therefore, uninformative and ambiguous contexts present a major challenge. In this paper, we investigate the use of an alternative instance representation based on lexical substitutes, i.e., contextually suitable, meaning-preserving replacements. Using lexical substitutes predicted by a state-of-the-art automatic system and a simple clustering algorithm, we outperform bag-of-words instance representations and compete with much more complex structured probabilistic models. Furthermore, we show that an oracle based on manually-labeled lexical substitutes yields yet substantially higher performance. Taken together, this provides evidence for a complementarity between word sense induction and lexical substitution that has not been given much consideration before.




How to Cite

Alagić, D., Šnajder, J., & Padó, S. (2018). Leveraging Lexical Substitutes for Unsupervised Word Sense Induction. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12017