REN Chaogan1,2, YANG Yan1,2, JIA Zhen1,2, TANG Huijia1, YU Xiuying1
1.School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756 2.Key Laboratory of Cloud Computing and Intelligent Technology of Sichuan Province, Southwest Jiaotong University, Chengdu 611756
Abstract:In question entity linking, a large amount of work in data processing and feature selection is required, cumulative errors are caused easily and the linking effect is reduced. To address the issues, an attention mechanism based encoder-decoder model for entity linking(AMEDEL) is proposed. In this model, long short-term memory network is utilized to encode the questions. Then, entity mentions and disambiguation information are generated as outputs through the decoder process by attention mechanism. Finally, these outputs are linked to the entities in knowledge base. The experiments are conducted on a dataset of questions and entities about products in automotive field. The results show that the proposed model obtains satisfactory results by only employing rare contextual information.
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