NDLI: Applying named entity recognition and co-reference resolution for segmenting English texts

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Applying named entity recognition and co-reference resolution for segmenting English texts

Content Provider	Springer Nature Link
Author	Fragkou, Pavlina
Copyright Year	2017
Abstract	In this paper we examine the benefit of performing named entity recognition (NER) and co-reference resolution to an English 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. Produced annotations were manually corrected and enriched to cover four types of named entities (person, location, date and group of names). 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 leads to the conclusion that, manual annotation appears to be a promising solution due to the obscure contribution of publicly available tools.
Starting Page	325
Ending Page	346
Page Count	22
File Format	PDF
ISSN	21926352
Journal	Progress in Artificial Intelligence
Volume Number	6
Issue Number	4
e-ISSN	21926360
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2017-05-09
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computational Intelligence Control, Robotics, Mechatronics Machine learning Data Mining and Knowledge Discovery Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Information extraction Natural language processing Text segmentation Computational linguistics Language Translation and Linguistics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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