Abstract
Given a specific information need, documents of the wrong genre can be considered as noise. From this perspective, genre classification helps to separate relevant documents from noise. Orthographic errors represent a second, finer notion of noise. Since specific genres often include documents with many errors, an interesting question is whether this “micro-noise” can help to classify genre. In this paper we consider both problems. After introducing a comprehensive hierarchy of genres, we present an intuitive method to build specialized and distinctive classifiers that also work for very small training corpora. Special emphasis is given to the selection of intelligent high-level features. We then investigate the correlation between genre and micro noise. Using special error dictionaries, we estimate the typical error rates for each genre. Finally, we test if the error rate of a document represents a useful feature for genre classification.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Document Analysis and Recognition (IJDAR)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
