Adapting the Naive Bayes Classifier to Rank Procedural Texts
This paper presents a machine-learning approach for ranking web documents according to the proportion of procedural text they contain. By ‘procedural text’ we refer to ordered lists of steps, which are very common in some instructional genres such as online manuals. Our initial training corpus is built up by applying some simple heuristics to select documents from a large collection and contains only a few documents with a large proportion of procedural texts. We adapt the Naive Bayes classifier to better fit this less than ideal training corpus. This adapted model is compared with several other classifiers in ranking procedural texts using different sets of features and is shown to perform well when only highly distinctive features are used.
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- 1.Clarke, C., Cormack, G., Laszlo, M., Lynam, T., Terra, E.: The Impact of Corpus Size on Question Answering Performance. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in IR, Tampere, Finland (1998)Google Scholar
- 2.Freund, Y., Mason, L.: The Alternating Decision Tree Learning Algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, pp. 124–133 (1999)Google Scholar
- 3.John, G., Langley, P.: Estimating Continuous Distributions in Bayesian Classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann, San Mateo (1995)Google Scholar
- 4.Kelly, D., Murdock, V., Yuan, X.J., Croft, W.B., Belkin, N.J.: Features of Documents Relevant to Task- and Fact-Oriented Questions. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM 2002), McLean, VA, pp. 645–647 (2002)Google Scholar
- 5.Santini, M.: A Shallow Approach to Syntactic Feature Extraction for Genre Classification. In: Proceedings of the 7th Annual Colloquium for the UK Special Interest Group for Computational Linguistics (CLUK 2004) (2004)Google Scholar
- 6.Schwitter, R., Rinaldi, F., Clematide, S.: The Importance of How-Questions in Technical Domains. In: Question-Answering workshop of TALN 2004, Fez, Morocco (2004)Google Scholar
- 8.Stricker, M., Vichot, F., Dreyfus, G., Wolinski, F.: Two Steps Feature Selection and Neural Network Classification for the TREC-8 Routing. CoRR cs. CL/0007016 (2000)Google Scholar
- 9.Takechi, M., Tokunaga, T., Matsumoto, Y., Tanaka, H.: Feature Selection in Categorizing Procedural Expressions. In: The Sixth International Workshop on Information Retrieval with Asian Languages (IRAL 2003), pp. 49–56 (2003)Google Scholar
- 10.Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Mateo (2000)Google Scholar
- 12.Yang, Y., Liu, X.: A Re-Examination of Text Categorization Methods. In: Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1999), pp. 42–49 (1999)Google Scholar
- 13.Yin, L.: A Two-Stage Approach to Retrieve Answers for How-To Questions. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics, Student session, Trento, Italy (2006)Google Scholar