One Representation per Word - Does it make Sense for Composition?
Thomas Kober, Julie Weeds, John Wilkie, Jeremy Reffin, David Weir
Abstract
In this paper, we investigate whether an a priori disambiguation of word senses is strictly necessary or whether the meaning of a word in context can be disambiguated through composition alone. We evaluate the performance of off-the-shelf single-vector and multi-sense vector models on a benchmark phrase similarity task and a novel task for word-sense discrimination. We find that single-sense vector models perform as well or better than multi-sense vector models despite arguably less clean elementary representations. Our findings furthermore show that simple composition functions such as pointwise addition are able to recover sense specific information from a single-sense vector model remarkably well.- Anthology ID:
- W17-1910
- 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:
- 79–90
- Language:
- URL:
- https://aclanthology.org/W17-1910
- DOI:
- 10.18653/v1/W17-1910
- Cite (ACL):
- Thomas Kober, Julie Weeds, John Wilkie, Jeremy Reffin, and David Weir. 2017. One Representation per Word - Does it make Sense for Composition?. In Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications, pages 79–90, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- One Representation per Word - Does it make Sense for Composition? (Kober et al., SENSE 2017)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/W17-1910.pdf
- Code
- tttthomasssss/sense2017