Exploring Influence of Topic Segmentation on Information Retrieval Quality

  • Gennady Shtekh
  • Polina KazakovaEmail author
  • Nikita Nikitinsky
  • Nikolay Skachkov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11193)


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.


Information retrieval Text segmentation Topic modeling Querying by example 



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.


  1. 1.
    Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)
  2. 2.
    Chan, S.K., Xie, L., Meng, H.: Modeling the statistical behavior of lexical chains to capture word cohesiveness for automatic story segmentation. In: Eighth Annual Conference of the International Speech Communication Association (2007)Google Scholar
  3. 3.
    Dai, A.M., Olah, C., Le, Q.V.: Document embedding with paragraph vectors. arXiv preprint arXiv:1507.07998 (2015)
  4. 4.
    Du, L., Buntine, W., Johnson, M.: Topic segmentation with a structured topic model. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 190–200 (2013)Google Scholar
  5. 5.
    Galley, M., McKeown, K.R., Fosler-Lussier, E., Jing, H.: Discourse segmentation of multi-party conversation. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (2003)Google Scholar
  6. 6.
    Galušcáková, P.: Application of topic segmentation in audiovisual information retrievalGoogle Scholar
  7. 7.
    Ganguly, D., Leveling, J., Jones, G.J.: Utilizing sub-topical structure of documents for information retrieval. In: Proceedings of the 4th Workshop on Workshop for Ph. D. Students in Information & Knowledge Management, pp. 75–78. ACM (2011)Google Scholar
  8. 8.
    Honnibal, M., Montani, I.: spaCy 2: natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing (2017, to appear)Google Scholar
  9. 9.
    Johnson, J., Douze, M., Jégou, H.: Billion-scale similarity search with GPUs. arXiv preprint arXiv:1702.08734 (2017)
  10. 10.
    Lau, J.H., Baldwin, T.: An empirical evaluation of doc2vec with practical insights into document embedding generation. arXiv preprint arXiv:1607.05368 (2016)
  11. 11.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)Google Scholar
  12. 12.
    Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., Joulin, A.: Advances in pre-training distributed word representations. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018) (2018)Google Scholar
  13. 13.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  14. 14.
    Pagliardini, M., Gupta, P., Jaggi, M.: Unsupervised learning of sentence embeddings using compositional n-gram features. arXiv preprint arXiv:1703.02507 (2017)
  15. 15.
    Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  16. 16.
    Prince, V., Labadié, A.: Text segmentation based on document understanding for information retrieval. In: Kedad, Z., Lammari, N., Métais, E., Meziane, F., Rezgui, Y. (eds.) NLDB 2007. LNCS, vol. 4592, pp. 295–304. Springer, Heidelberg (2007). Scholar
  17. 17.
    Riedl, M., Biemann, C.: Text segmentation with topic models. J. Lang. Technol. Comput. Linguist. 27(1), 47–69 (2012)Google Scholar
  18. 18.
    Skachkov, N., Vorontsov, K.: Improving topic models with segmental structure of texts. In: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference Dialogue, pp. 652–661 (2018)Google Scholar
  19. 19.
    Vorontsov, K., Frei, O., Apishev, M., Romov, P., Dudarenko, M.: BigARTM: open source library for regularized multimodal topic modeling of large collections. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds.) AIST 2015. CCIS, vol. 542, pp. 370–381. Springer, Cham (2015). Scholar
  20. 20.
    Vorontsov, K., Potapenko, A.: Additive regularization of topic models. Mach. Learn. 101(1–3), 303–323 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Gennady Shtekh
    • 1
  • Polina Kazakova
    • 1
    Email author
  • Nikita Nikitinsky
    • 2
  • Nikolay Skachkov
    • 3
  1. 1.National University of Science and Technology MISISMoscowRussia
  2. 2.Integrated SystemsMoscowRussia
  3. 3.Lomonosov Moscow State UniversityMoscowRussia

Personalised recommendations