5 research outputs found
Learning Rich Event Representations and Interactions for Temporal Relation Classification
International audienceMost existing systems for identifying temporal relations between events heavily rely on hand-crafted features derived from event words and explicit temporal markers. Besides, less attention has been given to automatically learning con-textualized event representations or to finding complex interactions between events. This paper fills this gap in showing that a combination of rich event representations and interaction learning is essential to more accurate temporal relation classification. Specifically, we propose a method in which i) Recurrent Neural Networks (RNN) extract contextual information ii) character embeddings capture morpho-semantic features (e.g. tense, mood, aspect), and iii) a deep Convolutional Neu-ral Network (CNN) finds out intricate interactions between events. We show that the proposed approach outperforms most existing systems on the commonly used dataset while using fully automatic feature extraction and simple local inference
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
There has been a steady need in the medical community to precisely extract
the temporal relations between clinical events. In particular, temporal
information can facilitate a variety of downstream applications such as case
report retrieval and medical question answering. Existing methods either
require expensive feature engineering or are incapable of modeling the global
relational dependencies among the events. In this paper, we propose a novel
method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic
Regularization and Global Inference (CTRL-PG) to tackle the problem at the
document level. Extensive experiments on two benchmark datasets, I2B2-2012 and
TB-Dense, demonstrate that CTRL-PG significantly outperforms baseline methods
for temporal relation extraction.Comment: 10 pages, 4 figures, 7 tables, accepted by AAAI 202