A Sentence-Level Joint Relation Classification Model Based on Reinforcement Learning. (27th May 2021)

• Record Type: Journal Article
• Authors: Liu, Zhen; Di, XiaoQiang; Song, Wei; Ren, WeiWu
• Other Names: Conforto Silvia Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2021(2021)
• Publication Date: 2021-05-27
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2021/5557184
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Relation classification is an important semantic processing task in the field of natural language processing (NLP). Data sources generally adopt remote monitoring strategies to automatically generate large-scale training data, which inevitably causes label noise problems. At the same time, another challenge is that important information can appear at any place in the sentence. This paper presents a sentence-level joint relation classification model. The model has two modules: a reinforcement learning (RL) agent and a joint network model. In particular, we combine bidirectional long short-term memory (Bi-LSTM) and attention mechanism as a joint model to process the text features of sentences and classify the relation between two entities. At the same time, we introduce an attention mechanism to discover hidden information in sentences. The joint training of the two modules solves the noise problem in relation extraction, sentence-level information extraction, and relation classification. Experimental results demonstrate that the model can effectively deal with data noise and achieve better relation classification performance at the sentence level.