Abstract
In this paper, we propose a new model that is capable of recognizing overlapping mentions. We introduce a novel notion of mention separators that can be effectively used to capture how mentions overlap with one another. On top of a novel multigraph representation that we introduce, we show that efficient and exact inference can still be performed. We present some theoretical analysis on the differences between our model and a recently proposed model for recognizing overlapping mentions, and discuss the possible implications of the differences. Through extensive empirical analysis on standard datasets, we demonstrate the effectiveness of our approach.- Anthology ID:
- D17-1276
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2608–2618
- Language:
- URL:
- https://aclanthology.org/D17-1276
- DOI:
- 10.18653/v1/D17-1276
- Cite (ACL):
- Aldrian Obaja Muis and Wei Lu. 2017. Labeling Gaps Between Words: Recognizing Overlapping Mentions with Mention Separators. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2608–2618, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Labeling Gaps Between Words: Recognizing Overlapping Mentions with Mention Separators (Muis & Lu, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/D17-1276.pdf