Research on knowledge concept extraction method based on few-shot learning and chain-of-thought prompting
Knowledge concept extraction has important application value in the fields of education, medical care, and finance. Knowledge concept extraction is a sub-task of named entity recognition. However, due to the lack of data sets and the particularity of knowledge concept entity types, directly applying...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2025-01-01
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| Series: | 大数据 |
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| Online Access: | http://www.j-bigdataresearch.com.cn/thesisDetails?columnId=109257829&Fpath=home&index=0 |
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| _version_ | 1849470648429051904 |
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| author | SHE Linlin XIONG Longyang LU Xuesong |
| author_facet | SHE Linlin XIONG Longyang LU Xuesong |
| author_sort | SHE Linlin |
| collection | DOAJ |
| description | Knowledge concept extraction has important application value in the fields of education, medical care, and finance. Knowledge concept extraction is a sub-task of named entity recognition. However, due to the lack of data sets and the particularity of knowledge concept entity types, directly applying general named entity recognition methods to knowledge concept extraction tasks often has poor results. In view of the above challenges, a method based on few-shot learning and chain-of-thought prompting for knowledge concept extraction was proposed, utilizing open-source large language models. Firstly, text representations focusing on entity semantics were trained through contrastive learning, and the relevance of the retrieved few-shot examples was enhanced using the <italic>K</italic>-nearest neighbors algorithm. Secondly, a method utilizing chain-of-thought prompting was adopted to present the samples, with the aim of improving the reasoning ability of large language models in knowledge concept extraction. Experimental results on multiple datasets demonstrate that the few-shot learning and chain-of-thought prompting for knowledge concept extraction method, onthe whole, has shown results superior over existing methods. |
| format | Article |
| id | doaj-art-72e00fbe4094466187c11d3a30fed516 |
| institution | Kabale University |
| issn | 2096-0271 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 大数据 |
| spelling | doaj-art-72e00fbe4094466187c11d3a30fed5162025-08-20T03:25:07ZzhoChina InfoCom Media Group大数据2096-02712025-01-01114109257829Research on knowledge concept extraction method based on few-shot learning and chain-of-thought promptingSHE LinlinXIONG LongyangLU XuesongKnowledge concept extraction has important application value in the fields of education, medical care, and finance. Knowledge concept extraction is a sub-task of named entity recognition. However, due to the lack of data sets and the particularity of knowledge concept entity types, directly applying general named entity recognition methods to knowledge concept extraction tasks often has poor results. In view of the above challenges, a method based on few-shot learning and chain-of-thought prompting for knowledge concept extraction was proposed, utilizing open-source large language models. Firstly, text representations focusing on entity semantics were trained through contrastive learning, and the relevance of the retrieved few-shot examples was enhanced using the <italic>K</italic>-nearest neighbors algorithm. Secondly, a method utilizing chain-of-thought prompting was adopted to present the samples, with the aim of improving the reasoning ability of large language models in knowledge concept extraction. Experimental results on multiple datasets demonstrate that the few-shot learning and chain-of-thought prompting for knowledge concept extraction method, onthe whole, has shown results superior over existing methods.http://www.j-bigdataresearch.com.cn/thesisDetails?columnId=109257829&Fpath=home&index=0named entity recognitionlarge language model |
| spellingShingle | SHE Linlin XIONG Longyang LU Xuesong Research on knowledge concept extraction method based on few-shot learning and chain-of-thought prompting 大数据 named entity recognition large language model |
| title | Research on knowledge concept extraction method based on few-shot learning and chain-of-thought prompting |
| title_full | Research on knowledge concept extraction method based on few-shot learning and chain-of-thought prompting |
| title_fullStr | Research on knowledge concept extraction method based on few-shot learning and chain-of-thought prompting |
| title_full_unstemmed | Research on knowledge concept extraction method based on few-shot learning and chain-of-thought prompting |
| title_short | Research on knowledge concept extraction method based on few-shot learning and chain-of-thought prompting |
| title_sort | research on knowledge concept extraction method based on few shot learning and chain of thought prompting |
| topic | named entity recognition large language model |
| url | http://www.j-bigdataresearch.com.cn/thesisDetails?columnId=109257829&Fpath=home&index=0 |
| work_keys_str_mv | AT shelinlin researchonknowledgeconceptextractionmethodbasedonfewshotlearningandchainofthoughtprompting AT xionglongyang researchonknowledgeconceptextractionmethodbasedonfewshotlearningandchainofthoughtprompting AT luxuesong researchonknowledgeconceptextractionmethodbasedonfewshotlearningandchainofthoughtprompting |