Abstract
Count-based distributional semantic models suffer from sparsity due to unobserved but plausible co-occurrences in any text collection. This problem is amplified for models like Anchored Packed Trees (APTs), that take the grammatical type of a co-occurrence into account. We therefore introduce a novel form of distributional inference that exploits the rich type structure in APTs and infers missing data by the same mechanism that is used for semantic composition.- Anthology ID:
- P17-2069
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 433–440
- Language:
- URL:
- https://aclanthology.org/P17-2069
- DOI:
- 10.18653/v1/P17-2069
- Cite (ACL):
- Thomas Kober, Julie Weeds, Jeremy Reffin, and David Weir. 2017. Improving Semantic Composition with Offset Inference. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 433–440, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Improving Semantic Composition with Offset Inference (Kober et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P17-2069.pdf
- Code
- tttthomasssss/acl2017