Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks
Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, Guergana Savova
Abstract
Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing. However, they might not be an ideal representation for time expressions, which are long, highly varied, and semantically complex. We describe a method for representing time expressions with single pseudo-tokens for CNNs. With this method, we establish a new state-of-the-art result for a clinical temporal relation extraction task.- Anthology ID:
- W17-2341
- Volume:
- BioNLP 2017
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada,
- Editors:
- Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 322–327
- Language:
- URL:
- https://aclanthology.org/W17-2341
- DOI:
- 10.18653/v1/W17-2341
- Cite (ACL):
- Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, and Guergana Savova. 2017. Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks. In BioNLP 2017, pages 322–327, Vancouver, Canada,. Association for Computational Linguistics.
- Cite (Informal):
- Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks (Lin et al., BioNLP 2017)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/W17-2341.pdf