Combining Information Extraction and Text Segmentation methods in Greek Texts

Abstract

This paper leverages semantic information that is elicited from information extraction techniques, to text segmentation algorithms. The purpose here is to examine whether semantic information boosts segmentation accuracy. Present study is performed in a Greek corpus. Semantic extraction is performed through an already existing NER tool for Greek (focusing on four named entity types) as well as (manually performed) co-reference resolution. Produced results reveal that, the proposed approach can be very promising in improving text segmentation performance as a result of extracting valuable semantic information. They also reveal that, manual annotation in specific information extraction tasks constitutes a unique option due to lack of freely available automatic annotation tools especially in languages such as Greek.