OF THESIS STEREOTYPING THE WEB: GENRE CLASSIFICATION OF WEB DOCUMENTS Retrieving relevant documents over the Web is a difficult task. Currently, search engines rely on keywords for matching documents to user queries. This paper explores the potential for discriminating documents based on the genre of the document. I define genre as a taxonomy that incorporates the style, form and content of a d...

Users assess the “appropriateness” of web documents in many ways. Traditionally, appropriateness has been solely a matter of relevance to a particular topic. But users are concerned with other aspects of document “genre”, such as the level of expertise assumed by the author, or the amount of detail. In previous work, we have used machine learning to automatically classify documents along a vari...

Taking an interdisciplinary and bimodal approach to genre analysis, this paper examines the influence of an IT system on a genre of technical communication: the technical trouble ticket. This genre is examined first as an organizational practice, and then as a set of rhetorical statements; finally, the two approaches are combined and the role of the system in creating, controlling and enabling ...

The digitalization of communication creates novel answers to familiar questions – in the form of digital products. What must be done to define the genres of digital products? What must be done to enable the user to identify the genre? Such questions are not new. Art history refers to and to with the methods of stylistics and iconology, while the role of divides fine ...

We approach the general problem of classifying machine-printed documents into genres. Layout is a critical factor in recognizing fine-grained genres, as document content features are similar. Document genre is determined from the layout structure detected from scanned binary images of the document pages, using no OCR results and minimal a priori knowledge of document logical structures. Our met...

Modern search engines aim at classifying web pages not only according to topics, but also according to genres. This paper presents the results of an attempt to train a genre classifier. We present features extracted from a 20-genre corpus used for training the genre classifiers and the results of using different machine learning (ML) algorithms in the process of learning. Success of the genre c...

Musical genres are categorical descriptions that are used to describe music. They are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music information retrieval. Genre categorization for audio has traditionally been performed manually. A particular musical genre is characterized by statistical properties related to the instr...

Genres of spoken and written texts are being intensively studied from various angles, e.g., communication studies, discourse analysis, computational linguistics, without arriving at a generally accepted definition. Many corpora have been built to represent the language, but very few large corpora indicate genres, and when they do the typology of genres varies widely. For instance, the Brown cor...

Constructing robust categorical and typological classifiers, i.e., finding auditory constructs utilized for describing music categories, is an important problem in music genre classification. Supervised methods such as support vector machine (SVM) achieve state of the art performance for genre classification but suffer from over-fitting on training examples. In this paper, we introduce a superv...

Recently a large amount of new chord annotations have been made available. This raises hopes for further development in automatic chord estimation. While more data seems to imply better performance, a major challenge however, is the wide variety of genres covered by these new data. As a result, the genre-independent training scheme as is common today is bound to fail. In this paper we investiga...