| 基于词性特征的特征权重计算方法 |
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| 引用本文： | 胡雯雯,高俊波,施志伟,刘志远. 基于词性特征的特征权重计算方法[J]. 计算机系统应用, 2018, 27(1): 92-97 |
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| 作者姓名： | 胡雯雯 高俊波 施志伟 刘志远 |
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| 作者单位： | 上海海事大学 信息工程学院, 上海 201306,上海海事大学 信息工程学院, 上海 201306,上海海事大学 信息工程学院, 上海 201306,上海海事大学 信息工程学院, 上海 201306 |
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| 摘 要： | 短文本因其具有特征稀疏、动态交错等特点，令传统的权重加权计算方法难以得到有效使用. 本文通过引入翻译决策模型，将某种词性出现的概率作为特征，提出一种新的基于词性特征的特征权重计算方法，并用文本聚类算法进行测试. 测试结果表明：与TF-IDF、QPSO两种权重计算算法相比，改进的特征权重计算算法取得更好的聚类效果.
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| 关 键 词： | 翻译决策模型 TDQO算法 词性 聚类 |
| 收稿时间： | 2017-03-24 |
| 修稿时间： | 2017-04-13 |
Feature Weight Calculation Method Based on Part of Speech Characteristics |
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| HU Wen-Wen,GAO Jun-Bo,SHI Zhi-Wei and LIU Zhi-Yuan. Feature Weight Calculation Method Based on Part of Speech Characteristics[J]. Computer Systems& Applications, 2018, 27(1): 92-97 |
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| Authors: | HU Wen-Wen GAO Jun-Bo SHI Zhi-Wei LIU Zhi-Yuan |
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| Affiliation: | College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China,College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China,College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China and College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China |
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| Abstract: | Because of the sparse and dynamic crisscross characteristics, the short text makes the weight of traditional weighted method difficult to use effectively. This paper presents a new feature weight calculation algorithm based on part of speech. This algorithm is the quantum particle swarm optimization algorithm introduced into translation decision model which can calculate the probability of a feature with certain part of speech. Then it is tested by the text clustering algorithm. The test results show that the improved feature weight calculation algorithm on the clustering accuracy is better than TF-IDF and QPSO algorithm. |
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| Keywords: | translation decision model TDQO algorithm part-of-speech clustering |
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