We consider methods for training a prosodic classifier using labeled training data from a different genre than the one on which the system will be deployed. Two binary tasks are considered: word-level pitch accent and phrase boundary detection. Using radio news and conversational telephone speech, we consider cross-genre training using acoustic and textual features, and find that acoustic featu...

This dissertation presents, discusses, and sheds some light on the problems that appear when computers try to automatically classify musical genres from audio signals. In particular, a method is proposed for the automatic music genre classification by using a computational approach that is inspired in music cognition and musicology in addition to Music Information Retrieval techniques. In this ...

We adapt the hierarchical attention network for the task of genre classification using lyrics. Utilising a large dataset of intact song lyrics we apply a recurrent neural network model which tries to learn importance of words, lines and sections in the genre classification task. This hierarchical structure attempts to replicate the structure of lyrics and enable learning of which sections, line...

Classification of musical genres gives a useful measure of similarity and is often the most useful descriptor of a musical piece. Previous techniques to use hidden Markov models (HMMs) for automatic genre classification have used a single HMM to model an entire song or genre. This paper provides a framework to give finer segmentation of HMMs through acoustic segment modeling. Modeling each of t...

In this paper, we present an inferential model for text type and genre identification of web pages, where text types are inferred using a modified form of Bayes’ theorem, and genres are derived using a few simple if-then rules. As the genre system on the web is a complex reality, and web pages are much more unpredictable and individualized than paper documents, we propose this approach as an al...

This paper presents experiments on classifying web pages by genre. Firstly, a corpus of 1539 manually labeled web pages was prepared. Secondly, 502 genre features were selected based on the literature and the observation of the corpus. Thirdly, these features were extracted from the corpus to obtain a data set. Finally, three machine learning algorithms, one for induction of decision trees (J48...

In this paper we propose a new centroid-based approach for genre categorization of web pages. Our approach constructs genre centroids using a set of genre-labeled web pages, called training web pages. The obtained centroids will be used to classify new web pages. The aim of our approach is to provide a flexible, incremental, refined and combined categorization, which is more suitable for automa...

Deliberation is an increasingly used concept in Argumentation Theory and Linguistic Analysis. But only recently research combined empirical and conceptual tool-boxes from these disciplines for the study of deliberative discourse. The aim of this article is to present a discursive analysis of deliberation as a genre using the relational discourse structure of texts. In particular, we want to see...

Current document retrieval tools succeed in locating large numbers of documents relevant to a given query. While search results may be relevant according to the topic of the documents, it is more difficult to identify which of the relevant documents are most suitable for a particular user. Automatic genre analysis that is, the ability to distinguish documents according to style would be a usefu...