Elements of a learning interface for genre qualified search
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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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.