Cross-domain named entity recognition based on embedding hierarchical vector into multi-cell compositional LSTM

Junchi Liu; Cui Zhu; Wenjun Zhu

doi:10.1117/12.2659993

3 February 2023 Cross-domain named entity recognition based on embedding hierarchical vector into multi-cell compositional LSTM

Junchi Liu, Cui Zhu, Wenjun Zhu

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Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 1251118 (2023) https://doi.org/10.1117/12.2659993
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

For unsupervised cross-domain named entity recognition, the texts of different domains have different features, and there are also a large number of domain-related specific vocabularies, resulting in some specific words of the target domain are rarely learned in source domain, or have different meanings. In order to solve above problems, we are the first to propose that embedding hierarchical vector representation into the multi-cell compositional LSTM-CRF model, sentence vectors are added on the basis of the character-word vector to form the character-word-sentence hierarchical vector representation. Based on the different contributions of different words to sentences, the model constructs sentence vectors by using label attention mechanism, so that sentence vectors can use more comprehensive information to infer the features of domain-related specific vocabularies, reduce its interference to model understanding. The multi-cell compositional LSTM model encodes various entities and uses the relationship between words and entities to transfer cross-domain knowledge from the word sequence level to the entity sequence level. Finally, after modifying the boundary of the label sequence through the CRF layer, the final result is obtained. In addition to the main task of named entity recognition, the model also uses LM (Language Modeling, LM) task to assist in learning the domain features of the target domain. The experimental results show that the F1 value of the model proposed in this paper has been greatly improved in the different cross-domain datasets.

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Junchi Liu, Cui Zhu, and Wenjun Zhu "Cross-domain named entity recognition based on embedding hierarchical vector into multi-cell compositional LSTM", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 1251118 (3 February 2023); https://doi.org/10.1117/12.2659993

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KEYWORDS

Data modeling

Performance modeling

Target recognition

Neural networks

Feature extraction

Convolution

Machine learning

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