摘要
随着新技术及社会网络的发展与普及,微博用户数据量剧增,与此相关的研究引起了学术界和工业界的关注。针对中文微博语句特点,通过对比多种特征选取方法,提出一种新的特征统计方法。根据构建的词语字典与词性字典,分析支持向量机、朴素贝叶斯、K最近邻等分类模型,并利用证据理论结合多分类器对中文微博观点句进行识别。采用中国计算机学会自然语言处理与中文计算会议(NLP&CC 2012)提供的数据,运用该方法得到的准确率、召回率和F值分别为70.6%、89.2%、78.9%,而NLP&CC2012公布的评测结果相应平均值分别为72.7%、61.5%、64.7%,该方法在召回率和F值2个指标上超过其平均值,而F值比NLP&CC2012评测结果的最好值高出0.5%。
With the development and popularity of the new technology and social network, the data volume of micro-blog users surge sharply. Related research causes increasing attention from both academia and industry. This paper proposes a new statistical method on feature extraction. Classification performances of different schemas such as Support Vector Machine（SVM）, Naive Bayes and K-Nearest Neighbour（KNN） are analyzed carefully. It proposes a combined model based on D-S theory to take the advantages of different classifiers. A series of experiments based on the Chinese Micro-Blog data provided by CCF NLP＆CC 2012 are conducted, and it gets the average estimate 72.7% in precision, 61.5% in recall and 64.7% in F-measure of NLP＆CC 2012 as a baseline. Experimental results show that the method can achieve significant enhancement in both recall and F-measure with 70.6%, 89.2% and 78.9%, respectively, and F-measure is even 0.5% higher than the best result of NLP＆CC 2012.
出处
《计算机工程》
CAS
CSCD
2014年第4期159-163,169,共6页
Computer Engineering
基金
国家自然科学基金资助项目(61170192)
关键词
微博
观点句
支持向量机
朴素贝叶斯
K近邻
证据理论
micro-blog
opinion sentence
Support Vector Machine（SVM）
Naive Bayes
K-Nearest Neighbour（KNN）
D-S theory
