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《Computer Engineering & Software》 2019-05
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Incorporating Paraphrase and Word-level Attention for Relation Detection

LI Kuan-yu;YUAN Jian;SHEN Ning-jing;School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology;  
In the knowledge base question answer system, due to the diversity and complexity of natural language expression, the question with the same semantic but different expressions may yield different answer. The generation of paraphrase can alleviate this problem. Secondly, relation detection is a crucial step in the knowledge base question answer system. The accuracy of the question answering system to answer questions is mainly affected by this step. The traditional attention-based relation detection model does not take into account the importance of different part of the different abstract levels of the answer path expression. Therefore, this paper proposes a relation detection model based on paraphrase and word-level attention mechanism, which is used in the knowledge base question answer system end task. Experiments show that the model has higher accuracy in answering questions.
【Fund】: 国家自然科学基金项目(批准号:61775139)
【CateGory Index】: TP391.1
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