Name
Abstract | ||
|---|---|---|
Document representation is one of the crucial components that determine the effectiveness of text classification tasks. Traditional document representation approaches typically adopt a popular bag-of-word method as the underlying document representation. Although itpsilas a simple and efficient method, the major shortcoming of bag-of-word representation is in the independent of word feature assumption. Many researchers have attempted to address this issue by incorporating semantic information into document representation. In this paper, we study the effect of semantic representation on the effectiveness of text classification systems. We employed a novel semantic smoothing technique to derive semantic information in a form of mapping probability between topic signatures and single-word features. Two classifiers, Naive Bayes and Support Vector Machine, were selected to carry out the classification experiments. Overall, our topic-signature semantic representation approaches significantly outperformed traditional bag-of-word representation in most datasets. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IJCNN.2008.4633926 | Neural Networks, 2008. IJCNN 2008. |
Keywords | Field | DocType |
Bayes methods,pattern classification,support vector machines,text analysis,Naive Bayes,bag-of-word representation,document representation,semantic information,semantic smoothing technique,support vector machine,text classification systems,topic signature mapping,topic-signature semantic representation | Semantic similarity,Representation term,Pattern recognition,Naive Bayes classifier,Computer science,Support vector machine,Explicit semantic analysis,Natural language processing,Artificial intelligence,Artificial neural network,MultiNet,Semantic computing | Conference |
ISSN | ISBN | Citations |
1098-7576 E-ISBN : 978-1-4244-1821-3 | 978-1-4244-1821-3 | 0 |
PageRank | References | Authors |
0.34 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Palakorn Achananuparp | 1 | 302 | 23.16 |
| Xiaohua Zhou | 2 | 438 | 25.82 |
| Xiaohua Hu | 3 | 2819 | 314.15 |
| Xiaodan Zhang | 4 | 429 | 22.61 |