Extracting Keywords from Government Work Reports with Multi-feature Fusion
Pan Huiping,Li Baoan,Zhang Le,Lv Xueqiang()
Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China
[Objective] This paper proposes a modified BiLSTM-CRF model to automatically extract keywords from the government work reports with the help of BERT word vector, Wubi features, domain synonyms, and word frequencies. [Methods] First, we used the BERT and Wubi vectors to capture the semantic and font features of the input sequence. Then, we captured the category features of the input sequence with the domain synonym table for the government work reports. Third, we assigned the word frequency features as weight to the word vector to capture context features of input sequence. Finally, we used the BiLSTM-CRF model to retrieve more semantic information and automatically extract keywords from government work reports. [Results] We examined the proposed model on the self-built corpus of government work reports. The precision, recall and F1 values reached 86.14%, 91.56%, and 88.42%. We also evaluated the validity of each feature in the model with the ablation experiment. [Limitations] More research is needed to utilize the model to other texts. [Conclusions] The proposed method could effectively extract keywords from Chinese texts.
潘慧萍, 李宝安, 张乐, 吕学强. 基于多特征融合的政府工作报告关键词提取研究*[J]. 数据分析与知识发现, 2022, 6(5): 54-63.
Pan Huiping, Li Baoan, Zhang Le, Lv Xueqiang. Extracting Keywords from Government Work Reports with Multi-feature Fusion. Data Analysis and Knowledge Discovery, 2022, 6(5): 54-63.
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