2018
DOI: 10.1007/978-3-319-91716-0_3
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Relation Extraction in Knowledge Base Question Answering: From General-Domain to the Catering Industry

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Cited by 8 publications
(9 citation statements)
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References 24 publications
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“…At present, the question answering method of knowledge base in Chinese domain is mainly improved based on information retrieval and vector modeling. Lai et al [8] used convolutional neural network to identify semantic features in questions and determined the results through the matching degree of answers and questions; Dai et al [9] proposed a method, which first carries out named entity recognition, then carries out attribute mapping through two-way LSTM [10] based on attention mechanism, and finally selects the answer from the knowledge base based on the results of the first two steps; Chen et al [11] proposed a relationship extraction method integrating artificial rules to improve the accuracy of relationship recognition.…”
Section: Related Workmentioning
confidence: 99%
“…At present, the question answering method of knowledge base in Chinese domain is mainly improved based on information retrieval and vector modeling. Lai et al [8] used convolutional neural network to identify semantic features in questions and determined the results through the matching degree of answers and questions; Dai et al [9] proposed a method, which first carries out named entity recognition, then carries out attribute mapping through two-way LSTM [10] based on attention mechanism, and finally selects the answer from the knowledge base based on the results of the first two steps; Chen et al [11] proposed a relationship extraction method integrating artificial rules to improve the accuracy of relationship recognition.…”
Section: Related Workmentioning
confidence: 99%
“…Here the candidates can be either entities or relations. Some use embedding-based models to predict answers directly (Dong et al, 2015;Bast and Haussmann, 2015;Hao et al, 2017;Zhou et al, 2018;Lukovnikov et al, 2017), whereas others focus on extracting relation paths and require further procedures to select the answer entity (Bordes et al, 2015;Xu et al, 2016;Yin et al, 2016;Yu et al, 2017;Zhang et al, 2018a;Yu et al, 2018;Chen et al, 2018a;Shen et al, 2018). Our work follows the latter methods in focusing on predicting relation paths, but we seek to eliminate the need to assume in advance a maximum number of hops.…”
Section: Related Workmentioning
confidence: 99%
“…Here the candidates can be either entities or relations. Some use embedding-based models to predict answers directly (Dong et al, 2015;Bast and Haussmann, 2015;Hao et al, 2017;Zhou et al, 2018;Lukovnikov et al, 2017), whereas others focus on extracting relation paths and require further procedures to select the answer entity (Bordes et al, 2015;Xu et al, 2016;Yin et al, 2016;Yu et al, 2017;Zhang et al, 2018a;Yu et al, 2018;Chen et al, 2018a;Shen et al, 2018). Our work follows the latter methods in focusing on predicting relation paths, but we seek to eliminate the need to assume in advance a maximum number of hops.…”
Section: Related Workmentioning
confidence: 99%