Automatic Genre-Specific Text Classification

INTRODUCTION Starting with a vast number of unstructured or semi-structured documents, text mining tools analyze and sift through them to present to users more valuable information specific to their information needs. The technologies in text mining include information extrac-In this chapter, we share our hands-on experience with one specific text mining task — text classification [Sebastiani, 2002]. Information occurs in various formats, and some formats have a specific structure or specific information that they contain: we refer to these as`genres'. Examples of information genres include news items, reports, academic articles, etc. In this paper, we deal with a specific genre type, course syllabus. A course syllabus is such a genre, with the following commonly-occurring fields: title, description, instructor's name, textbook details, class schedule, etc. In essence, a course syllabus is the skeleton of a course. Free and fast access to a collection of syllabi in a structured format could have a significant impact on education, especially for educators and lifelong learners. Educators can borrow ideas from others' syllabi to organize their own classes. It also will be easy for lifelong learners to find popular textbooks and even important chapters when they would like to learn a course on their own. Unfortunately, searching for a syllabus on the Web using Information Retrieval [Baeza-Yates & Ribeiro-Neto, 1999] techniques employed by a generic search engine often yields too many non-relevant search result pages (i.e., noise) — some of these only provide guidelines on syllabus creation; some only provide a schedule for a course event; some have outgoing links to syllabi (e.g. a course list page of an academic department). Therefore, a well-designed classifier for the search results is needed, that would help not only to filter noise out, but also to identify more relevant and useful syllabi. This chapter presents our work regarding automatic recognition of syllabus pages through text classification to build a syllabus collection. Issues related to the selection of appropriate features as well as classifier model construction using both generative models (Naïve) are discussed. Our results show that SVM outperforms NB in recognizing true syllabi.