Clause-Based Approach to Extracting Problem Phrases from User Reviews of Products

  • Vladimir IvanovEmail author
  • Elena Tutubalina
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 436)


This paper describes approaches to problem-phrase extraction from user reviews of products. The first step in problem extraction is to separate sentences with problems from all others. We propose two methods to problem extraction from such sentences: (i) a straightforward algorithm that does not split sentence into clauses and (ii) an improved clause-based algorithm. We claim that both approaches improve the classification performance compared to machine-learning algorithms.


Text classification Information extraction 



We are grateful to Valery Solovyev and Sergey Serebryakov for their support of this research, useful discussions and help with our approaches. We are grateful to the Program Committee members who provided constructive review comments. This work was partially supported by Russian Ministry of Education and Science (project number: 3056, “Semantic web technologies and linguistic databases: annotation, information extraction and retrieval”).


  1. 1.
    Liu, B., Hu, M.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04, New York, NY, USA (2004)Google Scholar
  2. 2.
    Lakoff, R.: If’s, and’s and but’s about conjunction. In: Fillmore, L. (eds.) Studies in Linguistic Semantics, New York (1971)Google Scholar
  3. 3.
    Gupta, N.: Extracting descriptions of problems with product and service from twitter data. In: Proceedings of the 3rd Workshop on Social Web Search and Mining, Beijing, China, July 2011Google Scholar
  4. 4.
    Gupta, N.: Extracting phrases describing problems with products and services from twitter messages. Technical report, Conference on Intelligent Text Processing and Computational Linguistics CICling 2013, March 2013Google Scholar
  5. 5.
    Winter, Y., Rimon, M.: Contrast and implication in natural language. J. Semant. 11, 365–406 (1994)CrossRefGoogle Scholar
  6. 6.
    Carreras, X., Màrquez, L.: Boosting trees for clause splitting. In: Proceedings of the CoNLL-2001 Shared Task, Toulouse, France (2001)Google Scholar
  7. 7.
    Gareev, R., Tkachenko, M., Solovyev, V., Simanovsky, A., Ivanov, V.: Introducing baselines for Russian named entity recognition. In: Gelbukh, A. (ed.) CICLing 2013, Part I. LNCS, vol. 7816, pp. 329–342. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Kazan (Volga Region) Federal UniversityKazanRussia
  2. 2.Institute of Informatics, Tatarstan Academy of SciencesKazanRussia

Personalised recommendations