| SSD模型及其在汉语词性标注中的应用 |
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| 引用本文： | 邢富坤,宋柔,罗智勇. SSD模型及其在汉语词性标注中的应用[J]. 中文信息学报, 2010, 24(1): 20-25 |
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| 作者姓名： | 邢富坤 宋柔 罗智勇 |
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| 作者单位： | 1. 北京语言大学 语言信息处理研究所,北京 100083; 2. 解放军外国语学院,河南 洛阳 471003 |
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| 基金项目： | 国家自然科学基金资助项目(60572159,60872121) |
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| 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。
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| 关 键 词： | 计算机应用 中文信息处理 SSD模型 HMM 词性标注 |
Symbol-and-Statistics Decoding Model and Its Application in Chinese POS Tagging |
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| XING Fukun,SONG Rou,LUO Zhiyong. Symbol-and-Statistics Decoding Model and Its Application in Chinese POS Tagging[J]. Journal of Chinese Information Processing, 2010, 24(1): 20-25 |
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| Authors: | XING Fukun SONG Rou LUO Zhiyong |
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| Affiliation: | 1. Center of Language Information Processing, Beijing Language and Culture University, Beijing 100083, China;
2. Foreign Languages University of PLA,Luoyang,Henan 471003,China |
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| Abstract: | 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... |
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| Keywords: | computer application Chinese information processing SSD model HMM POS tagging |
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