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A term extraction algorithm based on machine learning and comprehensive feature strategy

  • S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT 2022)
  • Published:
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Abstract

Manual term extraction is similar to literal meaning: A translator browses text, classifies words, and prepares for translation. Terminology, as a centralized carrier of expertise, creation, popularization, and disappearance, dynamically reflects the development and evolution of an industry. The automatic extraction of terminology is a key technology for creating a professional terminology database, and it is also a key topic in the field of natural language processing. The purpose of this paper is to study how to analyse a term extraction algorithm based on machine learning and a comprehensive feature strategy. Focusing on the problems of poor generality and single statistical features of current term extraction algorithms, this paper proposes an improved domain ontology term extraction algorithm based on a comprehensive feature strategy. Moreover, automatic term extraction experiments based on a word-based maximum entropy model and a conditional random field model based on machine learning are conducted in this paper. Its word-based conditional random field model outperforms the maximum entropy model. The experimental results show that the algorithm based on the comprehensive feature strategy improves the accuracy by 8.6% compared with the TF-IDF algorithm and the C-value term extraction algorithm. This algorithm can be used to effectively extract the terms in a text and has good generality.

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Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Funding

There is no funding for this study.

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Authors and Affiliations

  1. School of Cybersecurity, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Xiuliang Gong

  2. The Sixth Institute, 601 Branch of China Aeronautical Science and Technology Corporation, Hohhot, 010076, China

    Xiuliang Gong & Bo Cheng

  3. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Bo Cheng

  4. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Xiaomei Hu

  5. Shanghai Gewu Zhiyuan Network Technology Company Limited, Shanghai, 200082, China

    Wen Bo

Authors
  1. Xiuliang Gong
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  2. Bo Cheng
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  3. Xiaomei Hu
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  4. Wen Bo
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Corresponding author

Correspondence to Xiaomei Hu.

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Gong, X., Cheng, B., Hu, X. et al. A term extraction algorithm based on machine learning and comprehensive feature strategy. Neural Comput & Applic 36, 2385–2398 (2024). https://doi.org/10.1007/s00521-023-08960-9

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  • DOI: https://doi.org/10.1007/s00521-023-08960-9

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