【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．更多还原

【Abstract】 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.更多还原