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arXiv · 1912.03832

Effective Attention Modeling for Neural Relation Extraction

Tapas Nayak·Hwee Tou Ng
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Abstract

Relation extraction is the task of determining the relation between two entities in a sentence. Distantly-supervised models are popular for this task. However, sentences can be long and two entities can be located far from each other in a sentence. The pieces of evidence supporting the presence of a relation between two entities may not be very direct, since the entities may be connected via some indirect links such as a third entity or via co-reference. Relation extraction in such scenarios becomes more challenging as we need to capture the long-distance interactions among the entities and other words in the sentence. Also, the words in a sentence do not contribute equally in identifying the relation between the two entities. To address this issue, we propose a novel and effective attention model which incorporates syntactic information of the sentence and a multi-factor attention mechanism. Experiments on the New York Times corpus show that our proposed model outperforms prior state-of-the-art models.

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Tapas Nayak, Hwee Tou Ng. 2019-12-09. Effective Attention Modeling for Neural Relation Extraction. https://arxiv.org/abs/1912.03832

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