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| The public opinion has profoundly and extensively impacted the society, making the research on relation extraction(RE) in the field of public opinion crucial. Many existing relation extraction models either focus solely on basic information of Chinese characters or fail to fully leverage pre-trained models for extraction. Therefore, this paper proposes a relation extraction model, CwTransRE, which incorporates basic Chinese character information, glyph information, pinyin information and Chinese word information through transferring pre-trained models. CwTransRE enhances the effectiveness of relation extraction in two key aspects: firstly, besides basic Chinese character information, the integrated glyph, pinyin and Chinese word information enriches the semantic features of embeddings; secondly, the introduction of pre-trained models aids in obtaining more accurate embeddings, especially when dealing with relatively small training datasets. Experimental results on an open-source public opinion dataset demonstrate that our model achieves an F1 score of 0.703, outperforming NovelTagging, GraphRel(1p), GraphRel(2p) , TAG-JE and CasRel. |
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| Keywords:computer science and technology; public opinion; relation extraction; pre-trained model; multi-feature |
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