LexSemTm: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning
Andrew Bennett, Timothy Baldwin, Jey Han Lau, Diana McCarthy, Francis Bond
- Anthology ID:
- P16-1143
- Volume:
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2016
- Address:
- Berlin, Germany
- Editors:
- Katrin Erk, Noah A. Smith
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1513–1524
- Language:
- URL:
- https://aclanthology.org/P16-1143
- DOI:
- 10.18653/v1/P16-1143
- Cite (ACL):
- Andrew Bennett, Timothy Baldwin, Jey Han Lau, Diana McCarthy, and Francis Bond. 2016. LexSemTm: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1513–1524, Berlin, Germany. Association for Computational Linguistics.
- Cite (Informal):
- LexSemTm: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning (Bennett et al., ACL 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/P16-1143.pdf