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Method for Expert Search Using Topical Similarity of Documents

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1223))

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Abstract

The article describes the problem of finding and selecting experts for reviewing grant applications, proposals and scientific papers. The main shortcomings of the methods that are currently used to solve this problem were analyzed. These shortcomings can be eliminated by analyzing large collections of sci-tech documents, the authors of which are potential experts on various topics. The article describes a method that forms a ranked list of experts for a given document using a search for documents that are similar in topic. To evaluate the proposed method, we used a collection of grant applications from a science foundation. The proposed method is compared with the method based on topic modeling. Experimental studies show that in terms of such metrics as recall, MAP and NDCG, the proposed method is slightly better. In conclusion, the current limitations of the proposed method are discussed.

The research is supported by Russian Foundation for Basic Research (grant №18-29-03087) The reported research is also partially funded by the project “Text mining tools for big data” as a part of the program supporting Technical Leadership Centers of the National Technological Initiative “Center for Big Data Storage and Processing” at the Moscow State University (Agreement with Fund supporting the NTI-projects No. 13/1251/2018 11.12.2018).

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Notes

  1. 1.

    https://github.com/bigartm/bigartm version 0.10.0.

  2. 2.

    https://github.com/rare-technologies/gensim version 3.8.1.

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Authors and Affiliations

  1. Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, Moscow, Russia

    Denis Zubarev, Dmitry Devyatkin, Ilya Sochenkov, Ilya Tikhomirov & Oleg Grigoriev

  2. Moscow State University, Moscow, Russia

    Ilya Sochenkov

Authors
  1. Denis Zubarev
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  2. Dmitry Devyatkin
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  3. Ilya Sochenkov
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  4. Ilya Tikhomirov
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  5. Oleg Grigoriev
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Corresponding author

Correspondence to Denis Zubarev .

Editor information

Editors and Affiliations

  1. Kazan Federal University, Kazan, Russia

    Alexander Elizarov

  2. National Research University Higher School of Economics, St. Petersburg, Russia

    Boris Novikov

  3. Federal Research Center “Computer Science and Control”, Moscow, Russia

    Sergey Stupnikov

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Zubarev, D., Devyatkin, D., Sochenkov, I., Tikhomirov, I., Grigoriev, O. (2020). Method for Expert Search Using Topical Similarity of Documents. In: Elizarov, A., Novikov, B., Stupnikov, S. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2019. Communications in Computer and Information Science, vol 1223. Springer, Cham. https://doi.org/10.1007/978-3-030-51913-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-51913-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-51912-4

  • Online ISBN: 978-3-030-51913-1

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