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.
P. Kazakova, N. Nikitinsky and N. Skachkov—Equal contribution.
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Acknowledgements
We would like to acknowledge the commitment from Anton Lozhkov throughout this study. We are also thankful to Viktor Bulatov for help in editing the present paper.
This research was supported by the Ministry of Education and Science of the Russian Federation under the unique research id RFMEFI57917X0143.
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Shtekh, G., Kazakova, P., Nikitinsky, N., Skachkov, N. (2018). Exploring Influence of Topic Segmentation on Information Retrieval Quality. In: Bodrunova, S. (eds) Internet Science. INSCI 2018. Lecture Notes in Computer Science(), vol 11193. Springer, Cham. https://doi.org/10.1007/978-3-030-01437-7_11
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