Learning to classify documents according to genre

The idea of domain transfer—genre classifiers should be reusable across multiple topics—which does not arise in standard text classification is introduced and different features for building genre classifiers and their ability to transfer across multiple-topic domains are investigated.

Abstract

Abstract Current document‐retrieval tools succeed in locating large numbers of documents relevant to a given query. While search results may be relevant according to the topic of the documents, it is more difficult to identify which of the relevant documents are most suitable for a particular user. Automatic genre analysis (i.e., the ability to distinguish documents according to style) would be a useful tool for identifying documents that are most suitable for a particular user. We investigate the use of machine learning for automatic genre classification. We introduce the idea of domain transfer—genre classifiers should be reusable across multiple topics—which does not arise in standard text classification. We investigate different features for building genre classifiers and their ability to transfer across multiple‐topic domains. We also show how different feature‐sets can be used in conjunction with each other to improve performance and reduce the number of documents that need to be labeled.