  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 for
text segmentation. The aim here is to examine whether the combination of text
segmentation and information extraction can be beneficial for the
identification of the various topics that appear in a document. NER was
performed manually in the English corpus and was compared with the output
produced by publicly available annotation tools while, an already existing tool
was used for the Greek corpus. Produced annotations from both corpora were
manually corrected and enriched to cover four types of named entities.
Co-reference resolution i.e., substitution of every reference of the same
instance with the same named entity identifier was subsequently performed. The
evaluation, using five text segmentation algorithms for the English corpus and
four for the Greek corpus leads to the conclusion that, the benefit highly
depends on the segment's topic, the number of named entity instances appearing
in it, as well as the segment's length.
