摘要
为实现汉语全文词义自动标注,本文采用了一种新的基于无指导机器学习策略的词义标注方法．实验中建立了四个词义排歧模型,并对其测试结果进行了比较．其中实验效果最优的词义排歧模型融合了两种无指导的机器学习策略,并借助依存文法分析手段对上下文特征词进行选择．最终确定的词义标注方法可以使用大规模语料对模型进行训练,较好的解决了数据稀疏问题,并且该方法具有标注正确率高、扩展性能好等优点,适合大规模文本的词义标注工作．
For the purpose of implementing automatic Chinese word sense tagging, this paper presents a new method for word sense disambiguation based on unsupervised machine learning strategies. Four models of word sense disambiguation are built and compared. The model with two unsupervised machine learning strategies and selecting contextual features using dependence grammar obtains the best performance. And it can be trained with large-scale corpus to deal with the problem of data sparseness. In addition, it has such characteristics as high accuracy, high speed, easy extension and so on. Thus this technique is competent for word sense tagging on large-scale real-world text.
出处
《自动化学报》
EI
CSCD
北大核心
2006年第2期228-236,共9页
Acta Automatica Sinica
基金
国家自然科学基金重点项目(60435020)国家自然科学基金项目(60575042
60573072)资助~~
关键词
词义标注
无指导学习算法
单纯贝叶斯模型
依存文法
Sense tagging, unsupervised learning algorithm, naive Bayesian model, dependency grammar
