Name
Abstract | ||
|---|---|---|
Automatic Chinese text classification is an important and well-known research topic in the field of information retrieval and natural language processing. However, past researches often ignore the problem of word segmentation and the relationship between words. This paper proposes an N-gram-based language model for Chinese text classification which considers the relationship between words. To prevent from the out-of-vocabulary problem, a novel smoothing method based on logistic regression is also proposed to improve the performance. The experimental result shows that our approach outperforms the previous N-gram-based classification model above 11% on micro-average F-measure. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-12179-1_38 | ICCSA (3) |
Keywords | Field | DocType |
chinese text classification,automatic chinese text classification,micro-average f-measure,information retrieval,out-of-vocabulary problem,natural language processing,logistic regression,n-gram-based language model,n-gram model,previous n-gram-based classification model,language model,word segmentation,n gram,feature selection | Bag-of-words model,Feature selection,Computer science,Text segmentation,Smoothing,Natural language processing,n-gram,Language identification,Artificial intelligence,Logistic regression,Machine learning,Language model | Conference |
Volume | ISSN | ISBN |
6018 | 0302-9743 | 3-642-12178-0 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Show-Jane Yen | 1 | 537 | 130.05 |
| Yue-Shi Lee | 2 | 543 | 41.14 |
| Yu-Chieh Wu | 3 | 247 | 23.16 |
| Jia-Ching Ying | 4 | 34 | 3.18 |
| Vincent S. Tseng | 5 | 2923 | 161.33 |