Representations of time expressions for temporal relation extraction with convolutional neural networks
Published in Proceedings of the 16th BioNLP Workshop, 322-327, 2017, 2017
Recommended citation: Chen Lin, Timothy A Miller, Dmitriy Dligach, Steven Bethard, and Guergana K Savova. 2017. Representations of time expressions for temporal relation extraction with convolutional neural networks. In Proceedings of the 16th BioNLP Workshop, 322-327, 2017. https://aclanthology.org/W17-2341.pdf
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.