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| word combination kernel for text categorization | |
| Zhang Lujiang; Hu Xiaohui; Qin Shiyin | |
| 2012 | |
| Source | Journal of Digital Information Management
 |
| ISSN | 0972-7272 |
| Volume | 10Issue:3Pages:202-211 |
| English Abstract | We proposed a novel kernel for text categorization. This kernel is an inner product in the feature space generated by all word combinations of specified length. A word combination is a collection of different words co-occurring in the same sentence. The word combination of length k is weighted by the k-th root of the product of the inverse document frequencies (IDF) of its words. A computationally simple and efficient algorithm was proposed to calculate this kernel. By restricting the words of a word combination to the same sentence and considering multi-word combinations, the word combination features can capture similarity at a more specific level than single words. By discarding word order, the word combination features are more compatible with the flexibility of natural language and the dimensionality this kernel can be reduced significantly compared to the word-sequence kernel. We conducted a series of experiments on the Reuters-21578 dataset and 20 Newsgroups dataset. This kernel consistently achieves better performance than the classical word kernel and word-sequence kernel on the two datasets. We also assessed the impact of word combination length on performance and compared the computing efficiency of this kernel to those of the word kernel and word-sequence kernel.; We proposed a novel kernel for text categorization. This kernel is an inner product in the feature space generated by all word combinations of specified length. A word combination is a collection of different words co-occurring in the same sentence. The word combination of length k is weighted by the k-th root of the product of the inverse document frequencies (IDF) of its words. A computationally simple and efficient algorithm was proposed to calculate this kernel. By restricting the words of a word combination to the same sentence and considering multi-word combinations, the word combination features can capture similarity at a more specific level than single words. By discarding word order, the word combination features are more compatible with the flexibility of natural language and the dimensionality this kernel can be reduced significantly compared to the word-sequence kernel. We conducted a series of experiments on the Reuters-21578 dataset and 20 Newsgroups dataset. This kernel consistently achieves better performance than the classical word kernel and word-sequence kernel on the two datasets. We also assessed the impact of word combination length on performance and compared the computing efficiency of this kernel to those of the word kernel and word-sequence kernel. |
| Indexed Type | EI |
| Keyword | Algorithms Learning Systems Support Vector Machines |
| Department | (1) School of Automation Science and Electrical Engineering Beijing University of Aeronautics and Astronautics Beijing 100191 China; (2) Institute of Software Chinese Academy of Sciences Beijing 100190 China |
| Language | 英语 |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15034 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Zhang Lujiang,Hu Xiaohui,Qin Shiyin. word combination kernel for text categorization[J]. Journal of Digital Information Management,2012,10(3):202-211. |
| APA | Zhang Lujiang,Hu Xiaohui,&Qin Shiyin.(2012).word combination kernel for text categorization.Journal of Digital Information Management,10(3),202-211. |
| MLA | Zhang Lujiang,et al."word combination kernel for text categorization".Journal of Digital Information Management 10.3(2012):202-211. |
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