摘要
为了提高中文关键词提取的准确率和实用性,提出了一种改进了候选词权重计算的关键词提取算法TFLD(term frequency,lo-cation&distance algorithm),利用候选词权重排序自学习,提高了提取关键词算法的效率。该方法采用词语词频统计、分布区域以及词语距离位序3种特征项,并使用最小均平方(LMS)法则训练算法模型的调整因子。实验结果表明,该方法提高了关键词提取的精度。
Aiming at improving accuracy and practicality of key-phrase extraction for Chinese,a new algorithm was proposed,which named as TFLD(term frequency,location distance algorithm),the calculation accuracy by obtaining a sorted candidate keyword sequence was improved.Based on word frequency features including statistic of term frequency,term location and term distance,the least mean square(LMS) algorithm was trained to calculate the parameters for TFLD algorithm.The experimental results show that the proposed method improves the accuracy of key-phrase extraction in a considerable magnitude.
出处
《机电工程》
CAS
2010年第9期123-126,共4页
Journal of Mechanical & Electrical Engineering
关键词
关键词提取
中文文本
中文信息处理
key-words extraction
Chinese text
Chinese information processing