汉语语料词性标注自动校对方法的研究

兼类词的词类排歧是汉语语料词性标注中的难点问题,它严重影响语料的词性标注质量。针对这一难点问题,本文提出了一种兼类词词性标注的自动校对方法。它利用数据挖掘的方法从正确标注的训练语料中挖掘获取有效信息,自动生成兼类词词性校对规则,并应用获取的规则实现对机器初始标注语料的自动校对,从而提高语料中兼类词的词性标注质量。分别对50万汉语语料做封闭测试和开放测试,结果显示,校对后语料的兼类词词性标注正确率分别可提高11.32%和5.97%。

Research on the Method of Automatic Correction of Chinese Part-of-Speech Tagging

The disambiguation of multi-category words is one of the difficulties in part-of-speech tagging of Chinese text, which affects the processing quality of corpora greatly. Aiming at this question, the paper describes an approach to correcting the part-of-speech tagging of multi-category words automatically. It acquires correction rules for the part-of-speech tagging of multi-category words from right-tagged corpora based on the rough sets and data mining, and then corrects the corpora based on these rules automatically. According to the results of close-test and open-test on the corpus of 500,000 Chinese characters, the accuracy of multi-category words'part-of-speech tagging can be increased by 11.32% and 5.97% respectively.