摘要
近年来,如何从大量信息中提取关键信息已成为一个急需解决的问题。针对中文专利长文档,提出一种结合Word2Vec和TextRank的专利生成算法。首先利用Python Jieba技术对中文专利文档进行分词,利用停用词典去除无意义的词;其次利用Word2Vec算法进行特征提取,并利用WordCloud对提取的关键词进行可视化展示;最后利用TextRank算法计算语句间的相似度,生成摘要候选句,根据候选句的权重生成该专利文档的摘要信息。实验表明,采用Word2Vec和TextRank生成的专利摘要质量高,概括性也强。
In recent years, how to extract critical information from large amounts of information has become a problem which needs to be solved urgently. For Chinese patent long documents, a patent generation algorithm combining Word2Vec and TextRank is proposed.Firstly, Python Jieba technology is used to segment words in Chinese patent documents, and meaningless words are removed by using the stop dictionary. Secondly, the Word2Vec algorithm is used for feature extraction, and the extracted keywords are visually displayed by WordCloud. Finally, the TextRank algorithm is used to calculate the similarity between sentences, generate abstract candidate sentences,and generate abstract information of the patent documents according to the weight of candidate sentences. Experiments show that patent abstracts generated by Word2Vec and TextRank are of high quality and have strong generalization.
作者
朱玉婷
刘乐
辛晓乐
陈珑慧
康亮河
ZHU Yuting;LIU Le;XIN Xiaole;CHEN Longhui;KANG Lianghe(Gansu Agricultural University,Lanzhou 730070,China)
出处
《现代信息科技》
2023年第4期36-38,42,共4页
Modern Information Technology
基金
甘肃省农业大学盛彤笙科技创新基金(GSAU-STS-2021-15)
国家自然基金(32060437)
甘肃农业大学省级大学生创新创业训练计划项目(202216018)。
