Abstract
Relation extraction is an important task in NLP for knowledge graph and question answering. Traditional relation extraction models simply concatenate all the features as neural network model input, ignoring the different contribution of the features to the semantic representation of entities relations. In this paper, we propose a feature-level attention model to encode sentences, which tries to reveal the different effects of features for relation prediction. In the experiments, we systematically studied the effects of three strategies of attention mechanisms, which demonstrates that scaled dot product attention is better than others. Our experiments on real-world dataset demonstrate that the proposed model achieves significant and consistent improvement in the relation extraction task compared with baselines.
This paper was sponsored by Shanghai Sailing Program No. 19YF1402300, by the Initial Research Funds for Young Teachers of Donghua University No. 112-07-0053019.
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Dai, L., Xu, B., Song, H. (2019). Feature-Level Attention Based Sentence Encoding for Neural Relation Extraction. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11838. Springer, Cham. https://doi.org/10.1007/978-3-030-32233-5_15
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