Automatic Genre-Specific Text Classification

Automatic Genre-Specific Text Classification

Xiaoyan Yu (Virginia Tech, USA), Manas Tungare (Virginia Tech, USA), Weiguo Fan (Virginia Tech, USA), Manuel Pérez-Quiñones (Virginia Tech, USA), Edward A. Fox (Virginia Tech, USA), William Cameron (Villanova University, USA) and Lillian Cassel (Villanova University, USA)
Copyright: © 2009 |Pages: 8
DOI: 10.4018/978-1-60566-010-3.ch020
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Starting with a vast number of unstructured or semistructured 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 extraction, topic tracking, summarization, categorization/ classification, clustering, concept linkage, information visualization, and question answering [Fan, Wallace, Rich, & Zhang, 2006]. 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 life-long learners. Educators can borrow ideas from others’ syllabi to organize their own classes. It also will be easy for life-long 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.
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There has been recent interest in collecting and studying the syllabus genre. A small set of digital library course syllabi was manually collected and carefully analyzed, especially with respect to their reading lists, in order to define the digital library curriculum [Pomerantz, Oh, Yang, Fox, & Wildemuth, 2006]. In the MIT OpenCourseWare project, 1,400 MIT course syllabi were manually collected and made publicly available, which required a lot of work by students and faculty.

Some efforts have already been devoted to automating the syllabus collection process. A syllabus acquisition approach similar to ours is described in [Matsunaga, Yamada, Ito, & Hirokaw, 2003]. However, their work differs from ours in the way syllabi are identified. They crawled Web pages from Japanese universities and sifted through them using a thesaurus with common words which occur often in syllabi. A decision tree was used to classify syllabus pages and entry pages (for example, a page containing links to all the syllabi of a particular course over time). Similarly, [Thompson, Smarr, Nguyen, & Manning, 2003] used a classification approach to classify education resources – especially syllabi, assignments, exams, and tutorials. Using the word features of each document, the authors were able to achieve very good performance (F1 score: 0.98). However, this result is based upon their relative clean data set, only including the four kinds of education resources, which still took efforts to collect. We, on the other hand, to better apply to a variety of data domains, test and report our approach on search results for syllabi on the Web.

In addition, our genre feature selection work is also inspired by research on genre classification, which aims to classify data according to genre types by selecting features that distinguish one genre from another, i.e., identifying home pages in sets of web pages [Kennedy & Shepherd, 2005].

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