Abstract
Usage similarity (USim) is an approach to determining word meaning in context that does not rely on a sense inventory. Instead, pairs of usages of a target lemma are rated on a scale. In this paper we propose unsupervised approaches to USim based on embeddings for words, contexts, and sentences, and achieve state-of-the-art results over two USim datasets. We further consider supervised approaches to USim, and find that although they outperform unsupervised approaches, they are unable to generalize to lemmas that are unseen in the training data.- Anthology ID:
- W17-1906
- Volume:
- Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Jose Camacho-Collados, Mohammad Taher Pilehvar
- Venue:
- SENSE
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 47–52
- Language:
- URL:
- https://aclanthology.org/W17-1906
- DOI:
- 10.18653/v1/W17-1906
- Cite (ACL):
- Milton King and Paul Cook. 2017. Supervised and unsupervised approaches to measuring usage similarity. In Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications, pages 47–52, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Supervised and unsupervised approaches to measuring usage similarity (King & Cook, SENSE 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W17-1906.pdf