In this paper, we discuss the notion of genre as a basis for addressing the problem of context representation in digital preservation. We outline several reference points for the notion of genre. This includes a review of diplomatic principles that can support and enhance the power of genre as a key to capture information about context relations. Further, we discuss the impact of open genre mod...

Modern search engines are typically queried with keywords, which foremostly convey the topic of the sought web page. Consequently the resulting top hits are often topically relevant, but nonetheless not what the user wants. The premise of this paper is that the relevance of the hits can be improved when also searching by genre, classification criterion orthogonal to topic. To this end a genre c...

This paper examines the potential of high-level features extracted from symbolic musical representations in regards to musical classification. Twenty features are implemented and tested by using them to classify 225 MIDI files by genre. This system differs from previous automatic genre classification systems, which have focused on low-level features extracted from audio data. Files are classifi...

In this paper, two experiments in automatic genre classification of web pages are presented. These two experiments are designed to highlight three important issues related to genre classification: corpus composition and genre palettes, feature representativeness, and exportability of classification models. Results show the influence of corpus composition and genre palette on classification rate...

We explore the integration of multiple factors such as genre and speaker gender for acoustic model adaptation tasks to improve Mandarin ASR system performance on broadcast news and broadcast conversation audio. We investigate the use of multifactor clustering of acoustic model training data and the application of MPE-MAP and fMPE-MAP acoustic model adaptations. We found that by effectively comb...

Domain adaptation is an active field of research in statistical machine translation (SMT), but so far most work has ignored the distinction between the topic and genre of documents. In this paper we quantify and disentangle the impact of genre and topic differences on translation quality by introducing a new data set that has controlled topic and genre distributions. In addition, we perform a d...

The present study puts SFL-based genre approach under examination for its effectiveness in promoting EFL writers’ genre awareness and writing competence in a 16-week semester in a state-run college in the east part of China. Three sets of data from preand post-test writings, interviews and questionnaires, are collected and analyzed with the help of the software SPSS and Range 32. The study indi...

We hypothesized that the attributes of a document’s genre determine a document’s ability to be identified uniquely. Consequently, recognizing the genre will facilitate effective user-document interaction. In this pilot study, we exposed fifteen participants to a set of paper and digital documents, each converted into two surrogates: one based on form, in which the text was masked leaving only t...

Abstract Genre recognition is a critical facet of text comprehension and text classification. In three experiments, we assessed the minimum number of words in a sentence needed for genre recognition to occur, the distribution of genres across text, and the relationship between reading ability and genre recognition. We also propose and demonstrate a computational model for genre recognition. Usi...

Every text has at least one topic and at least one genre. Evidence for a text’s topic and genre comes, in part, from its lexical and syntactic features—features used in both Automatic Topic Classification and Automatic Genre Classification (AGC). Because an ideal AGC system should be stable in the face of changes in topic distribution, we assess five previously published AGC methods with respec...