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Unsupervised Joint Entity Linking over Question Answering Pair with Global Knowledge

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (NLP-NABD 2017, CCL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10565))

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Abstract

We consider the task of entity linking over question answering pair (QA-pair). In conventional approaches of entity linking, all the entities whether in one sentence or not are considered the same. We focus on entity linking over QA-pair, in which question entity and answer entity are no longer fully equivalent and they are with the explicit semantic relation. We propose an unsupervised method which utilizes global knowledge of QA-pair in the knowledge base(KB). Firstly, we collect large-scale Chinese QA-pairs and their corresponding triples in the knowledge base. Then mining global knowledge such as the probability of relation and linking similarity between question entity and answer entity. Finally integrating global knowledge and other basic features as well as constraints by integral linear programming(ILP) with an unsupervised method. The experimental results show that each proposed global knowledge improves performance. Our best F-measure on QA-pairs is 53.7%, significantly increased 6.5% comparing with the competitive baseline.

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Notes

  1. 1.

    https://www.Wikidata.org/wiki/Wikidata:Main_Page.

  2. 2.

    https://www.quora.com/.

  3. 3.

    http://www.answers.com/Q/.

  4. 4.

    https://zhidao.baidu.com/.

  5. 5.

    http://nlp.cs.rpi.edu/kbp/2014/scoring.html.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (No. 61533018) and the National Basic Research Program of China (No. 2014CB340503). And this research work was also supported by Google through focused research awards program.

Author information

Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Cao Liu, Shizhu He, Hang Yang, Kang Liu & Jun Zhao

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Cao Liu & Jun Zhao

Authors
  1. Cao Liu
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  2. Shizhu He
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  3. Hang Yang
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  4. Kang Liu
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  5. Jun Zhao
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Corresponding author

Correspondence to Cao Liu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xiaojie Wang

  3. Peking University, Beijing, China

    Baobao Chang

  4. Soochow University, Suzhou, China

    Deyi Xiong

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Liu, C., He, S., Yang, H., Liu, K., Zhao, J. (2017). Unsupervised Joint Entity Linking over Question Answering Pair with Global Knowledge. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_23

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  • DOI: https://doi.org/10.1007/978-3-319-69005-6_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69004-9

  • Online ISBN: 978-3-319-69005-6

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