Named Entity Recognition Model Based on Feature Fusion
Named entity recognition can deeply explore semantic features and enhance the ability of vector representation of text data. This paper proposes a named entity recognition method based on multi-head attention to aim at the problem of fuzzy lexical boundary in Chinese named entity recognition. Firstl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/14/2/133 |
| _version_ | 1797620336675520512 |
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| author | Zhen Sun Xinfu Li |
| author_facet | Zhen Sun Xinfu Li |
| author_sort | Zhen Sun |
| collection | DOAJ |
| description | Named entity recognition can deeply explore semantic features and enhance the ability of vector representation of text data. This paper proposes a named entity recognition method based on multi-head attention to aim at the problem of fuzzy lexical boundary in Chinese named entity recognition. Firstly, Word2vec is used to extract word vectors, HMM is used to extract boundary vectors, ALBERT is used to extract character vectors, the Feedforward-attention mechanism is used to fuse the three vectors, and then the fused vectors representation is used to remove features by BiLSTM. Then multi-head attention is used to mine the potential word information in the text features. Finally, the text label classification results are output after the conditional random field screening. Through the verification of WeiboNER, MSRA, and CLUENER2020 datasets, the results show that the proposed algorithm can effectively improve the performance of named entity recognition. |
| first_indexed | 2024-03-11T08:39:48Z |
| format | Article |
| id | doaj.art-7493bbdab4774b92abff84c5a23c1b37 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T08:39:48Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-7493bbdab4774b92abff84c5a23c1b372023-11-16T21:12:46ZengMDPI AGInformation2078-24892023-02-0114213310.3390/info14020133Named Entity Recognition Model Based on Feature FusionZhen Sun0Xinfu Li1School of Cyberspace Security and Computer Science, Hebei University, Baoding 071000, ChinaSchool of Cyberspace Security and Computer Science, Hebei University, Baoding 071000, ChinaNamed entity recognition can deeply explore semantic features and enhance the ability of vector representation of text data. This paper proposes a named entity recognition method based on multi-head attention to aim at the problem of fuzzy lexical boundary in Chinese named entity recognition. Firstly, Word2vec is used to extract word vectors, HMM is used to extract boundary vectors, ALBERT is used to extract character vectors, the Feedforward-attention mechanism is used to fuse the three vectors, and then the fused vectors representation is used to remove features by BiLSTM. Then multi-head attention is used to mine the potential word information in the text features. Finally, the text label classification results are output after the conditional random field screening. Through the verification of WeiboNER, MSRA, and CLUENER2020 datasets, the results show that the proposed algorithm can effectively improve the performance of named entity recognition.https://www.mdpi.com/2078-2489/14/2/133named entity recognitionALBERTvector fusionmultiple head attention |
| spellingShingle | Zhen Sun Xinfu Li Named Entity Recognition Model Based on Feature Fusion Information named entity recognition ALBERT vector fusion multiple head attention |
| title | Named Entity Recognition Model Based on Feature Fusion |
| title_full | Named Entity Recognition Model Based on Feature Fusion |
| title_fullStr | Named Entity Recognition Model Based on Feature Fusion |
| title_full_unstemmed | Named Entity Recognition Model Based on Feature Fusion |
| title_short | Named Entity Recognition Model Based on Feature Fusion |
| title_sort | named entity recognition model based on feature fusion |
| topic | named entity recognition ALBERT vector fusion multiple head attention |
| url | https://www.mdpi.com/2078-2489/14/2/133 |
| work_keys_str_mv | AT zhensun namedentityrecognitionmodelbasedonfeaturefusion AT xinfuli namedentityrecognitionmodelbasedonfeaturefusion |