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2018 | OriginalPaper | Chapter

Exploring Influence of Topic Segmentation on Information Retrieval Quality

Authors : Gennady Shtekh, Polina Kazakova, Nikita Nikitinsky, Nikolay Skachkov

Published in: Internet Science

Publisher: Springer International Publishing

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Abstract

In the present paper we address the issue of how an information retrieval system might be improved via text segmentation and to what extent. We assume that topic text segmentation allows one to better model text structure and therefore language itself, which influences the quality of text representation. We propose a search pipeline based on text segmentation by means of BigARTM tool and TopicTiling algorithm. We test the initial hypothesis by conducting experiments with several baseline models on two textual collections. The results are rather contradictory: while one collection showed that segmentation does improve the quality of retrieval, the other one demonstrated that segmentation does not influence the quality significantly.

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previous chapter A Self-organized Method for Computing the Epidemic Threshold in Computer Networks

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Title: Exploring Influence of Topic Segmentation on Information Retrieval Quality
Authors: Gennady Shtekh
Polina Kazakova
Nikita Nikitinsky
Nikolay Skachkov
Publisher: Springer International Publishing
Book: Internet Science
Print ISBN: 978-3-030-01436-0

Electronic ISBN: 978-3-030-01437-7

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-01437-7_11

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