基于规则的汉语兼类词标注方法  

Rule-based tagging method of Chinese ambiguity words

作　　者：李华栋[1] 贾真[1] 尹红风[1] 杨燕[1] 

机构地区：[1]西南交通大学信息科学技术学院,成都610031

出　　处：《计算机应用》2014年第8期2197-2201,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61134002;61170111;61202043;61262058)

摘　　要：针对目前汉语兼类词标注的准确率不高的问题,提出了规则与统计模型相结合的兼类词标注方法。首先,利用隐马尔可夫、最大熵和条件随机场3种统计模型进行兼类词标注;然后,将改进的互信息算法应用到词性(POS)标注规则的获取上,通过计算目标词前后词单元与目标词的相关性获得词性标注规则;最后,将获取的规则与基于统计模型的词性标注算法结合起来进行兼类词标注。实验结果表明加入规则算法之后,平均词性标注准确率提升了5%左右。Concerning the low accuracy of tagging Chinese ambiguity words, a combined tagging method of rules and statistical model was proposed in this paper. Firstly, three kinds of traditional statistical models, including Hidden Markov Model （HMM）, Maximum Entropy （ME） and Condition Random Field （CRF）, were used to tagging problem of the ambiguity words. Then, the improved mutual information algorithm was applied to learn Part Of Speech （POS） tagging rules. Tagging rules were got through the calculation of correlation between the target words and the nearby word units. Finally, rules were combined with statistical model algorithm to tag Chinese ambiguity words. The experimental results show that after adding the rule algorithm, the average accuracy of POS tagging promotes by 5%.

关 键 词：词性标注 互信息 汉语兼类词 规则 中文信息处理 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]