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Leveraging Document-Specific Information for Classifying Relations in Scientific Articles

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New Frontiers in Artificial Intelligence (JSAI-isAI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10838))

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Abstract

Tremendous amount of knowledge is present in the ever-growing scientific literature. In order to grasp this massive amount knowledge, various computational tasks are proposed for training computers to read and analyze scientific documents. As one of these task, semantic relationship classification aims at automatically analyzing semantic relationships in scientific documents. Conventionally, only a limited number of commonly used knowledge bases such as Wikipedia are used for collecting background information for this task. In this work, we hypothesize that scientific papers also could be utilized as a source of background information for semantic relationship classification. Based on the hypothesis, we propose the model that is capable of extracting background information from unannotated scientific papers. Preliminary experiments on the RANIS dataset [1] proves the effectiveness of the proposed model on relationship classification in scientific articles.

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Notes

  1. 1.

    The phrase target entity refers not merely the concept denoted by noun or noun phrase, but it could be an action denoted by a verb or verb phrase and some quality denoted by an adjective, adverb, etc.

  2. 2.

    This example is taken from W13-2242, ACL anthology (http://aclanthology.info).

  3. 3.

    https://code.google.com/archive/p/word2vec/.

  4. 4.

    We used the Stanford CoreNLP. https://stanfordnlp.github.io/CoreNLP/.

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Acknowledgement

This work was supported by JST CREST Grant Number JPMJCR1513, Japan and KAKENHI Grant Number 16H06614.

Author information

Authors and Affiliations

  1. Tohoku University, Sendai, Japan

    Qin Dai, Naoya Inoue & Kentaro Inui

  2. RIKEN Center for Advanced Intelligence Project, Wako, Japan

    Paul Reisert & Kentaro Inui

Authors
  1. Qin Dai
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  2. Naoya Inoue
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  3. Paul Reisert
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  4. Kentaro Inui
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Corresponding author

Correspondence to Qin Dai .

Editor information

Editors and Affiliations

  1. Chiba University, Chiba, Japan

    Sachiyo Arai

  2. National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan

    Kazuhiro Kojima

  3. Ochanomizu University, Tokyo, Japan

    Koji Mineshima

  4. Ochanomizu University, Tokyo, Japan

    Daisuke Bekki

  5. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

  6. Fujitsu Laboratories Limited, Kanagawa, Japan

    Yuiko Ohta

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Dai, Q., Inoue, N., Reisert, P., Inui, K. (2018). Leveraging Document-Specific Information for Classifying Relations in Scientific Articles. In: Arai, S., Kojima, K., Mineshima, K., Bekki, D., Satoh, K., Ohta, Y. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2017. Lecture Notes in Computer Science(), vol 10838. Springer, Cham. https://doi.org/10.1007/978-3-319-93794-6_26

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  • DOI: https://doi.org/10.1007/978-3-319-93794-6_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93793-9

  • Online ISBN: 978-3-319-93794-6

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