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中文摘要：
基于统计的文本相似度量方法大多先采用TF-IDF方法将文本表示为词频向量,然后利用余弦计算文本之间的相似度。此类方法由于忽略文本中词项的语义信息,不能很好地反映文本之间的相似度。基于语义的方法虽然能够较好地弥补这一缺陷,但需要知识库来构建词语之间的语义关系。研究了以上两类文本相似度计算方法的优缺点,提出了一种新颖的文本相似度量方法,该方法首先对文本进行预处理,然后挑选TF-IDF值较高的词项作为特征项,再借助HowNet语义词典和TF-IDF方法对特征项进行语义分析和词频统计相结合的文本相似度计算,最后利用文本相似度在基准文本数据集合上进行聚类实验。实验结果表明,采用提出的方法得到的F-度量值明显优于只采用TF-IDF方法或词语语义的方法,从而证明了提出的文本相似度计算方法的有效性。
英文摘要：
Based on the statistical text similarity measurements method used TF-IDF method to model text documents as term frequency vectors,and computed similarity between documents by using cosine similarity.This method ignored semantic information of text documents,the similarity value wasn＇t correct.Although based on semantics method made up for the drawback,but need of knowledge to construct the relationship between words.By studying the advantages and disadvantages of two kinds of methods,this paper presented a novel text similarity method,which firstly pre-processed text,then chose the terms with higher TF-IDF value as the feature items,next used semantic dictionary and TF-IDF method to compute the text similarity,finally used several K-means clustering methods for evaluating performance of the new text document similarity.Experimental results show that the method＇s F-measure is superior to the others＇ which proves that the proposed method is effective.
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