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2018 | OriginalPaper | Buchkapitel

Joint Detection of Topic Entity and Relation for Simple Question Answering

verfasst von : Yunqi Qiu, Yuanzhuo Wang, Xiaolong Jin

Erschienen in: Knowledge Science, Engineering and Management

Verlag: Springer International Publishing

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Abstract

Knowledge Base is a machine-readable set composed of well-structured relation information between entities, and has become an essential role in automatic question answering. There are two components significant to Knowledge Base Question Answering, i.e., topic entity detection which aims to find out the entity of interest in a given question, and relation detection which aims to find out the relations relevant to the question. Traditional methods decouple these two components, ignoring the correspondence between them. In this paper, we propose a neural attention-based model, namely, Joint Detection Network, to simultaneously detect topic entities and relations for simple question answering. This model can be trained in an end-to-end manner with weak supervision. Experimental results demonstrate the effectiveness of the proposed method.

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Vorheriges Kapitel Trust-Distrust Aware Recommendation by Integrating Metric Learning with Matrix Factorization

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Titel: Joint Detection of Topic Entity and Relation for Simple Question Answering
verfasst von: Yunqi Qiu
Yuanzhuo Wang
Xiaolong Jin
Verlag: Springer International Publishing
Buch: Knowledge Science, Engineering and Management
Print ISBN: 978-3-319-99246-4

Electronic ISBN: 978-3-319-99247-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-99247-1_33

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