Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks

By Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, Guergana K. Savova. 2017

39 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Nanyun Peng
2. Rujun Han
3. Qiang Ning
4. Dan Roth
5. Matt Gardner
6. Xiang Ren
7. Hao Wu
8. Hannaneh Hajishirzi

Citations in the corpus

• TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions (2020, background): “…Cham-bers et al., 2014; Ning et al., 2018a), structured learning (Leeuwenberg and Moens, 2017; Ning et al., 2017), and neural networks (Dligach et al., 2017; Lin et al., 2017; Tourille et al., 2017; Cheng and Miyao, 2017; Meng and Rumshisky, 2018; Leeuwenberg and Moens, 2018; Ning et al., 2019).”
• Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference (2020, unknown): “…and Uzuner 2013; Xu et al. 2013; Tang et al. 2013; Lee et al. 2016; Chikka 2016) such as SVMs, MaxEnt and CRFs, and neural network based methods (Lin et al. 2017, 2018; Dligach et al. 2017; Tourille et al. 2017; Lin et al. 2019; Guan et al. 2020; Lin et al. 2020; Galvan-Sosa et al. 2020).”