Abstract
Prepostitional phrase (PP) attachment is a well known challenge to parsing. In this paper, we combine the insights of different works, namely: (1) treating PP attachment as a classification task with an arbitrary number of attachment candidates; (2) using auxiliary distributions to augment the data beyond the hand-annotated training set; (3) using topological fields to get information about the distribution of PP attachment throughout clauses and (4) using state-of-the-art techniques such as word embeddings and neural networks. We show that jointly using these techniques leads to substantial improvements. We also conduct a qualitative analysis to gauge where the ceiling of the task is in a realistic setup.- Anthology ID:
- E17-2050
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 311–317
- Language:
- URL:
- https://aclanthology.org/E17-2050
- DOI:
- Cite (ACL):
- Daniël de Kok, Jianqiang Ma, Corina Dima, and Erhard Hinrichs. 2017. PP Attachment: Where do We Stand?. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 311–317, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- PP Attachment: Where do We Stand? (de Kok et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/E17-2050.pdf