基于融合词性的BiLSTM-CRF的期刊关键词抽取方法  

Keyword Extraction for Journals Based on Part-of-Speech and BiLSTM-CRF Combined Model

作　　者：成彬 施水才[1,2] 都云程 肖诗斌 Cheng Bin;Shi Shuicai;Du Yuncheng;Xiao Shibin(Computer School,Beijing Information Science&Technology University,Beijing 100185,China;Beijing TRS Information Technology Co.,Ltd.,Beijing 100101,China)

机构地区：[1]北京信息科技大学计算机学院,北京100185 [2]北京拓尔思信息技术股份有限公司,北京100101

出　　处：《数据分析与知识发现》2021年第3期101-108,共8页Data Analysis and Knowledge Discovery

摘　　要：【目的】利用CRF模型处理序列标注问题的优势,通过将词性信息和CRF模型融入BiLSTM网络,实现期刊关键词的自动抽取。【方法】将关键词抽取问题视为一个序列标注问题。对期刊文本进行分词和词性标注的预处理;对预处理后的文本使用Word2Vec模型进行Word Embedding向量化,获取字词的向量表达式;使用BiLSTM-CRF模型进行关键词的自动抽取。【结果】使用融合词性的BiLSTM-CRF网络,在采集的知网期刊文本上进行实验,在简单关键词方面,准确率较原始的BiLSTM模型提升3%;在复杂关键词方面,准确率较原始的BiLSTM模型提升12%。【局限】期刊关键词抽取模型无法准确抽取复杂关键词,需要针对复杂关键词层面进一步提升模型性能。【结论】融合词性的BiLSTM-CRF模型与传统方法相比,具有较高的识别准确率,是一种有效的关键词抽取方法。[Objective] Utilizing the advantages of the CRF model to solve the problem of sequence labeling, by incorporating part-of-speech information and the CRF model into the BiLSTM network, automatic extraction of journal keywords is realized. [Methods] The keyword extraction problem is considered as a sequence labeling problem. Pre-processing word segmentation and part-of-speech tagging of journal text;vectorizing the preprocessed text using the Word2Vec model for Word Embedding to obtain vector expressions of words;using BiLSTM-CRF model for automatic keyword extraction. [Results] Using the part-of-speech and BiLSTM-CRF network to perform experiments on the collected China National Knowledge Infrastructure text, the accuracy on Simple Word is improved by 3% compared to the original BiLSTM model. On Complex Word, the accuracy is improved by 12%. [Limitations] The journal keyword extraction model cannot accurately extract complex keywords. In future work, it is necessary to further remind the model of the performance of complex keywords.[Conclusions] Compared with the traditional method, the BiLSTM-CRF model with part-of-speech integration has higher recognition accuracy and is an effective keyword extraction method.

关 键 词：抽取 条件随机场 深度学习 双向长短期记忆网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]