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| high accuracy Chinese predicate recognition method combining lexical and syntactic feature |
| HAN Lei, LUO Sen-lin, PAN Li-min, WEI Chao |
| School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China |
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Abstract A method which merges lexical with syntactic features and combines C4.5 algorithm and rules was proposed for the systematic study of Chinese predicates. The method extracts lexical and syntactic features respectively. According to the lexical features, the suspicious predicate and its number are obtained. The lexical features are filtered by manual rules to identify the predicate which conforms to the rules. Basing on the lexical and syntactic features, the ones which do not conform to the rules are identified using C4.5. On the basis of Beijing forest studio-chinese tagged corpus（BFS-CTC） whose total number of predicates is more than 20 000, features and parameter choice experiment, syntactic features verification experiment, the amount of training data choice experiment and the method precision experiment were carried out to study the relations between predicate recognition results and the factors including lexical and syntactic features,function of the syntactic features and the amount of training data. The results show that the syntactic features effectively improves the effect of predicate recognition, as the amount of training data increasing the precision is convergence and the high precision reaches 99%.
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Published: 01 December 2014
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融合词法和句法特征的汉语谓词高精度识别方法
为了对汉语谓词进行系统的研究,提出一种融合词法和句法特征、结合C4.5机器学习和规则进行谓词识别的方法.该方法对句子的词法信息和句法信息分别进行特征提取,通过词法特征提取得到句子中可疑谓词及其个数,使用人工总结规则对词法特征进行规则过滤,对符合规则条件的样本直接给出结果,融合不符合规则样本的词法和句法特征,使用C4.5进行分类得到谓词识别结果.实验中,采用谓词总量达到20 000条以上的BFS-CTC标注语料库进行特征和参数选择、句法特征验证、训练数据量选择和算法准确性等一系列的实验，对谓词识别效果的影响进行研究.结果表明：句法特征能有效提升谓词识别效果,随着训练数据量的增加谓词识别准确率趋于平缓,达到了99%的高准确率.
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