Learning to recognize webpage genres
Information Processing and Management: an International Journal
DocuBrowse: faceted searching, browsing, and recommendations in an enterprise context
Proceedings of the 15th international conference on Intelligent user interfaces
Testing a genre-enabled application: a preliminary assessment
FDIA'08 Proceedings of the 2nd BCS IRSG conference on Future Directions in Information Access
Genre analysis of structured e-mails for corpus profiling
IRSG'08 Proceedings of the 2008 BCS-IRSG conference on Corpus Profiling
Building a document genre corpus: a profile of the KRYS I corpus
IRSG'08 Proceedings of the 2008 BCS-IRSG conference on Corpus Profiling
Cross-lingual genre classification
EACL '12 Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics
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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.