Federico Nanni

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Segmenting text into semantically coherent fragments improves readability of text and facilitates tasks like text summariza-tion and passage retrieval. In this paper , we present a novel unsupervised algorithm for linear text segmentation (TS) that exploits word embeddings and a measure of semantic relatedness of short texts to construct a semantic(More)
Introduction Humanities scholars have experimented with the potential of different text mining techniques for exploring large corpora, from co­occurrence­based methods to sequence­labeling algorithms (e.g. Named entity recognition). LDA topic modeling (Blei et al., 2003) has become one of the most employed approaches (Meeks and Weingart, 2012). Scholars(More)
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