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In this paper we examine the benefit of performing named entity recognition(NER) and co-reference resolution to an English and a Greek corpus used fortext segmentation. The aim here is to examine whether the combination of textsegmentation and information extraction can be beneficial for theidentification of the various topics that appear in a document. NER wasperformed manually in the English corpus and was compared with the outputproduced by publicly available annotation tools while, an already existing toolwas used for the Greek corpus. Produced annotations from both corpora weremanually corrected and enriched to cover four types of named entities.Co-reference resolution i.e., substitution of every reference of the sameinstance with the same named entity identifier was subsequently performed. Theevaluation, using five text segmentation algorithms for the English corpus andfour for the Greek corpus leads to the conclusion that, the benefit highlydepends on the segment's topic, the number of named entity instances appearingin it, as well as the segment's length.

Text Segmentation using Named Entity Recognition and Co-reference Resolution in English and Greek Texts