中文摘要：

针对How Net语义词典对词项收录数量的有限性在一定程度上制约文本相似度运算准确性的问题，提出一种词项语义维度映射的方法。从词项词性的角度出发，按词性对短文本中词项进行切分，按词性特征对短文本之间进行词项归并，构建词性向量，依据词频和 How Net语义词典，词项完成词性向量中权值映射，将短文本之间相似度运算转换为词性向量之间相似度运算。将该算法运用于信箱测试数据集，实验结果表明，该算法提高了文本相似度运算的准确率和相似度平均值。

英文摘要：

The accuracy of text similarity calculation is greatly restricted because of the limited amount of terms included in How‐Net semantic library .A way of term mapping with semantic space was proposed ,where short text was divided into several terms according to part of speech ,all the terms in both of the texts were merged together to constitute part of speech vector ,the map‐ping weight of each term in the part of speech vector was acquired according to term frequency and HowNet semantic library ,the similarity calculation between short texts was turned into the similarity calculation between part of speech vectors .The results of experiment on an open benchmark dataset of the mail show the proposed algorithm improves the accuracy and average similarity value compared with the traditional algorithm .