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Natural language processing algorithms for domain-specific data extraction in material science: Reseractor

  • Computation & theory
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Abstract

With the advent of several tools and web engines trained for finding journal articles out of billions of research papers on millions of topics in different databases with a high degree of generalizability, it often leads to a loss of specificity. Scientific pursuits need a tool to extract data from selected resources for performing domain-specific tasks. Current algorithms and generalized tools lack specificity and are challenged by errors in analysing data from a bundle of specific documents selected eclectically. Current work addresses the need for such a tool, which focuses on specificity based on users' input keywords and phrases to find relevant information from bundles of articles from the web. Reseractor is based on a customized algorithm, Whitespace, in synergy with output from open-access tools for document image analysis and focused domain data extraction using NLP. The current tool is designed for the material science domain with the features of adopting various generalized and scientific corpora as layers. It is tested on two sets of different bundles of papers and gives an accuracy of 81.12% along with a recall of 78.38% and a precision of 84.06%. Owing to the simple and direct applicability of algorithms, users from other domains can directly use their corpora in algorithms and remodel the tool for their purpose. Current work fulfills the need for domain-specific experimental data extraction stored in organized and structured databases for upcoming computational researchers.

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Data availability

Pseudocode for the tool, Reseractor is available in supplementary document (Online resource 1: section S3). The code is uploaded in GitHub repository [31] and relevant data used in this work are available from the corresponding author upon reasonable request on mail or website https://home.iitk.ac.in/~skjha/.

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Acknowledgements

We thank the Ministry of Education, Government. of India for supporting this work under Prime Minister Research Fellowship (PMRF) endowed to the author. We thank the Department of Materials Science and Engineering, IIT Kanpur, for their facilities and staff support. We would like to thank the developers of Layout Parser and Tesseract for providing their tools as open access where we could use our algorithm in synergy with theirs to contribute to document image analysis methods and domain-specific natural language processing.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Indian Institute of Technology, Kanpur, India

    Antrakrate Gupta, Divyansh Mittal, Ojsi Goel & Shikhar Krishn Jha

Authors
  1. Antrakrate Gupta
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  2. Divyansh Mittal
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  3. Ojsi Goel
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  4. Shikhar Krishn Jha
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Contributions

Antrakrate Gupta contributed to conceptualization, methodology (equal), formal analysis (equal), writing, investigation (equal), methodology (equal), visualization (equal), funding acquisition (equal). Divyansh Mittal contributed to coding, methodology (supporting), formal analysis (equal), writing (supporting), visualization (equal), and software. Ojsi Goel contributed to coding and methodology for Whitespace algorithm proposed above (equal). Shikhar Krishn Jha* contributed to conceptualization, methodology (equal), writing—review & editing, project administration, funding acquisition (equal) and supervision.

Corresponding author

Correspondence to Shikhar Krishn Jha.

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The authors have no conflicts to disclose.

Supplementary Information

The video “Reseractor_tool_video.mpg” is available online with the article. It shows preliminary working function of developed tool based on algorithm described in the article.

Additional information

Handling Editor: Ghanshyam Pilania.

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Supplementary information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 6863 KB)

Supplementary file2 (MPG 14032 KB)

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Gupta, A., Mittal, D., Goel, O. et al. Natural language processing algorithms for domain-specific data extraction in material science: Reseractor. J Mater Sci 59, 13856–13872 (2024). https://doi.org/10.1007/s10853-024-09980-z

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