Abstract
Relation Classification as a foundational task with regard to many other natural language processing (NLP) tasks, has caught many attentions in recent years. In this paper, we propose a novel network architecture called Attention-Based Improved Bidirectional Long Short-Term Memory and Convolutional Neural Network (AI-BLSTM-CNN) for this task. To be specific, we take improved BLSTM that makes the utmost of sequential context information and word information in order to obtain temporal features and high-level contextual representation. Besides, attention mechanism is applied to improved BLSTM making it focus on the segments of a sentence related to the relation automatically. Finally, we take advantage of CNN to capture the local important features for relation classification. The experimental results on SemEval-2010 Task 8 and KBP37 benchmark datasets show that AI-BLSTM-CNN achieves better performance than the majority of existing methods.
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Xiao, Q., Gao, M., Wu, S., Sun, X. (2019). Attention-Based Improved BLSTM-CNN for Relation Classification. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_4
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DOI: https://doi.org/10.1007/978-3-030-30490-4_4
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