欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
SVMTool是建立在支持向量机（SVM）原理上的序列标注工具，具有简单、灵活、高效的特点，可以融入大量的语言特征。该文将SVMTool应用于中文词性标注任务，将基于隐马尔科夫模型的基线系统准确率提升了2．07％。针对未登录词准确率不高的问题，该文加入了中文字、词的特征，包括构成汉字的部首特征和词重叠特征，并从理论上分析了这两个特征的可行性，实验显示加入这些特征后，未登录词标注的准确率提升了1．16％，平均错误率下降了7．40％。
英文摘要：
The SVMTool is a simple, flexible and effective generator of sequential tagger based on Support Vector Machines, capable of dealing with a large number of linguistic features. In this paper, SVMTool is applied in Chinese POS tagging task and improves the accuracy by 2. 07% compared with the baseline system on the Hidden Markov Model. To further improve the accuracy of unknown words, we introduce some features of Chinese characters and words, such as radicals of Chinese characters and reduplicate words, and probe into a theoretical analysis for their feasibility. Experiments indicate that these features can improve the accuracy of unknown words by 1.16% as well as reduce the error rate by 7. 40%.
同期刊论文项目
同项目期刊论文
期刊信息
位置：