Name
Abstract | ||
|---|---|---|
This paper investigates the correlation between features of three types (visual, stylistic and topical types) and genre classes. The majority of previous studies in automated genre classification have created models based on an amalgamated representation of a document using a combination of features. In these models, the inseparable roles of different features make it difficult to determine a means of improving the classifier when it exhibits poor performance in detecting selected genres. In this paper we use classifiers independently modeled on three groups of features to examine six genre classes to show that the strongest features for making one classification is not necessarily the best features for carrying out another classification. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/HICSS.2008.157 | HICSS |
Keywords | Field | DocType |
poor performance,best feature,genre classification,previous study,genre class,strongest feature,examining variations,different feature,automated genre classification,prominent features,topical type,inseparable role,amalgamated representation,feature extraction,text analysis,image classification | Pattern recognition,Document image processing,Computer science,Feature extraction,Artificial intelligence,Classifier (linguistics),Contextual image classification | Conference |
ISBN | Citations | PageRank |
0-7695-3075-8 | 6 | 0.46 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunhyong Kim | 1 | 89 | 8.98 |
| Seamus Ross | 2 | 38 | 8.53 |