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    <titleInfo>
        <title>Hybrid Question Answering over Knowledge Base and Free Text</title>
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        <namePart type="given">Kun</namePart>
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        <namePart type="given">Yansong</namePart>
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    <name type="personal">
        <namePart type="given">Songfang</namePart>
        <namePart type="family">Huang</namePart>
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    <name type="personal">
        <namePart type="given">Dongyan</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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    <abstract>Recent trend in question answering (QA) systems focuses on using structured knowledge bases (KBs) to find answers. While these systems are able to provide more precise answers than information retrieval (IR) based QA systems, the natural incompleteness of KB inevitably limits the question scope that the system can answer. In this paper, we present a hybrid question answering (hybrid-QA) system which exploits both structured knowledge base and free text to answer a question. The main challenge is to recognize the meaning of a question using these two resources, i.e., structured KB and free text. To address this, we map relational phrases to KB predicates and textual relations simultaneously, and further develop an integer linear program (ILP) model to infer on these candidates and provide a globally optimal solution. Experiments on benchmark datasets show that our system can benefit from both structured KB and free text, outperforming the state-of-the-art systems.</abstract>
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        <url>https://aclanthology.org/C16-1226</url>
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        <date>2016-12</date>
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%0 Conference Proceedings
%T Hybrid Question Answering over Knowledge Base and Free Text
%A Xu, Kun
%A Feng, Yansong
%A Huang, Songfang
%A Zhao, Dongyan
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F xu-etal-2016-hybrid
%X Recent trend in question answering (QA) systems focuses on using structured knowledge bases (KBs) to find answers. While these systems are able to provide more precise answers than information retrieval (IR) based QA systems, the natural incompleteness of KB inevitably limits the question scope that the system can answer. In this paper, we present a hybrid question answering (hybrid-QA) system which exploits both structured knowledge base and free text to answer a question. The main challenge is to recognize the meaning of a question using these two resources, i.e., structured KB and free text. To address this, we map relational phrases to KB predicates and textual relations simultaneously, and further develop an integer linear program (ILP) model to infer on these candidates and provide a globally optimal solution. Experiments on benchmark datasets show that our system can benefit from both structured KB and free text, outperforming the state-of-the-art systems.
%U https://aclanthology.org/C16-1226
%P 2397-2407

Markdown (Informal)

[Hybrid Question Answering over Knowledge Base and Free Text](https://aclanthology.org/C16-1226) (Xu et al., COLING 2016)

• Hybrid Question Answering over Knowledge Base and Free Text (Xu et al., COLING 2016)

ACL

• Kun Xu, Yansong Feng, Songfang Huang, and Dongyan Zhao. 2016. Hybrid Question Answering over Knowledge Base and Free Text. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2397–2407, Osaka, Japan. The COLING 2016 Organizing Committee.