Elements of a Learning Interface for Genre Qualified Search
Even prior to content, the genre of a web document leads to a first coarse binary classification of the recall space in relevant and non-relevant documents. Thinking of a genre search engine, massive data will be available via explicit or implicit user feedback. These data can be used to improve and to customize the underlying classifiers. A taxonomy of user behaviors is applied to model different scenarios of information gain. Elements of such a learning interface, as for example the implications of the lingering time and the snippet genre recognition factor, are discussed.
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- 1.Stubbe, A., Ringlstetter, C., Schulz, K.U.: Genre as noise: Noise in genre. International Journal on Document Analysis and Recognition (to appear, 2007)Google Scholar
- 4.Santini, M.: Common criteria for genre classification: Annotation and granularity. In: Workshop on Text-based Information Retrieval (TIR 2006), Riva del Garda, Italy (2006)Google Scholar
- 5.Joachims, T.: A statistical learning learning model of text classification for support vector machines. In: SIGIR 2001. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 128–136. ACM Press, New York, NY, USA (2001)CrossRefGoogle Scholar
- 6.Schohn, G., Cohn, D.: Less is more: Active learning with support vector machines. In: ICML 2000: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 839–846. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2000)Google Scholar