摘要
该文提出了一种以符号解码与数值解码并举的SSD(Symbol-and-Statistics Decoding Model)模型,该模型被用于汉语词性标注任务,其标注正确率在封闭测试中达到97.08%,开放测试中达到95.67%,较二阶HMM的95.56%和94.70%都有较为显著提高。SSD模型的正确率虽然不及最大熵模型和CRF模型,但它的训练时间远少于后者,说明SSD模型在处理自然语言中的特定任务时是一种较强的实用模型。
A statistical language model named Symbol-and-Statistics Decoding （SSD） language model is presented in this article. The 2-gram SSD model is applied to the Chinese POS tagging task with a quite good result. The precision is as high as 97. 08% in the closed test and 95.67% in the open test is, which are both significantly higher than the HMM at 95.56% and 94.70%, respectively. Although the performance of SSD model is not as good as the conditional models such as Maximum Entropy Model and CRF model, the training time of SSD is much less than the conditional models, which makes SSD model more applicable to certain tasks in natural language processing.
出处
《中文信息学报》
CSCD
北大核心
2010年第1期20-24,共5页
Journal of Chinese Information Processing
基金
国家自然科学基金资助项目(60572159
60872121)
