A Psychological and Computational Study of Sub-Sentential Genre Recognition

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. Using corpora of narrative, history, and science sentences, we found that readers could recognize the genre of over 80% of the sentences and that recognition generally occurred within the first three words of sentences; in fact, 51% of the sentences could be correctly identified by the first word alone. We also report findings that many texts are heterogeneous in terms of genre. That is, around 20% of text appears to include sentences from other genres. In addition, our computational models fit closely the judgments of human result. This study offers a novel approach to genre identification at the sub-sentential level and has important implications for fields as diverse as reading comprehension and computational text classification.