Survey on terminology extraction from texts
Abstract Automatic extraction of domain-related terminology from natural language texts is an important research topic with many practical application scenarios, such as text summarization, knowledge graph construction. It has a great impact on improving the quality of topic construction and the acc...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-025-01077-x |
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| _version_ | 1823861981503815680 |
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| author | Kang Xu Yifan Feng Qiandi Li Zhenjiang Dong Jianxiang Wei |
| author_facet | Kang Xu Yifan Feng Qiandi Li Zhenjiang Dong Jianxiang Wei |
| author_sort | Kang Xu |
| collection | DOAJ |
| description | Abstract Automatic extraction of domain-related terminology from natural language texts is an important research topic with many practical application scenarios, such as text summarization, knowledge graph construction. It has a great impact on improving the quality of topic construction and the accuracy of semantic retrieval. In recent years, automatic terminology extraction (ATE) has attracted widespread attention from scholars, and rich research results have been achieved. In this paper, we present a survey of terminology extraction from natural language texts. The survey encompasses definitions of pertinent issues and concepts, a systematic classification of proposed methodologies, an exploration of the associated datasets and tools, among other aspects. To the best of our knowledge, this is the first review that systematically summarizes the work of terminology extraction based on language models (LMs), offering comprehensive guidance resources for researchers and practitioners in the field. Consequently, this review offers valuable insights for researchers interested in terminology extraction issues within the Natural Language Processing (NLP) domain. |
| format | Article |
| id | doaj-art-8d2770b26e264e5e9b7e0b65770003fb |
| institution | Kabale University |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-8d2770b26e264e5e9b7e0b65770003fb2025-02-09T12:41:21ZengSpringerOpenJournal of Big Data2196-11152025-02-0112114010.1186/s40537-025-01077-xSurvey on terminology extraction from textsKang Xu0Yifan Feng1Qiandi Li2Zhenjiang Dong3Jianxiang Wei4School of Computer Science, Nanjing University of Posts and TelecommunicationSchool of Computer Science, Nanjing University of Posts and TelecommunicationSchool of Computer Science and Engineering, Southeast UniversitySchool of Computer Science, Nanjing University of Posts and TelecommunicationSchool of Management, Nanjing University of Posts and TelecommunicationAbstract Automatic extraction of domain-related terminology from natural language texts is an important research topic with many practical application scenarios, such as text summarization, knowledge graph construction. It has a great impact on improving the quality of topic construction and the accuracy of semantic retrieval. In recent years, automatic terminology extraction (ATE) has attracted widespread attention from scholars, and rich research results have been achieved. In this paper, we present a survey of terminology extraction from natural language texts. The survey encompasses definitions of pertinent issues and concepts, a systematic classification of proposed methodologies, an exploration of the associated datasets and tools, among other aspects. To the best of our knowledge, this is the first review that systematically summarizes the work of terminology extraction based on language models (LMs), offering comprehensive guidance resources for researchers and practitioners in the field. Consequently, this review offers valuable insights for researchers interested in terminology extraction issues within the Natural Language Processing (NLP) domain.https://doi.org/10.1186/s40537-025-01077-xTerminology extractionTerminology recognitionSurveyNatural language processing |
| spellingShingle | Kang Xu Yifan Feng Qiandi Li Zhenjiang Dong Jianxiang Wei Survey on terminology extraction from texts Journal of Big Data Terminology extraction Terminology recognition Survey Natural language processing |
| title | Survey on terminology extraction from texts |
| title_full | Survey on terminology extraction from texts |
| title_fullStr | Survey on terminology extraction from texts |
| title_full_unstemmed | Survey on terminology extraction from texts |
| title_short | Survey on terminology extraction from texts |
| title_sort | survey on terminology extraction from texts |
| topic | Terminology extraction Terminology recognition Survey Natural language processing |
| url | https://doi.org/10.1186/s40537-025-01077-x |
| work_keys_str_mv | AT kangxu surveyonterminologyextractionfromtexts AT yifanfeng surveyonterminologyextractionfromtexts AT qiandili surveyonterminologyextractionfromtexts AT zhenjiangdong surveyonterminologyextractionfromtexts AT jianxiangwei surveyonterminologyextractionfromtexts |