摘要: Digital humanities scholars are developing new techniques of literary study using non-consumptive processing of large collections of scanned text. A crucial step in working with such collections is to separate the main text of a w... 展开 Digital humanities scholars are developing new techniques of literary study using non-consumptive processing of large collections of scanned text. A crucial step in working with such collections is to separate the main text of a work from the surrounding paratext, the content of which may distort word counts, location references, sentiment scores, and other important outputs. Simple heuristic methods have been devised, but are not accurate for some texts and some methodological needs. This study describes a method for paratext detection based on smoothed unsupervised clustering. We show that this method is more accurate than simple heuristics, especially for non-fiction works, and edited works with larger amounts of paratext. We also show that a more accurate detection of paratext boundaries improves the accuracy of subsequent text processing, as exemplified by a readability metric. 收起