Various Criteria of Collocation Cohesion in Internet: Comparison of Resolving Power

  • Igor A. Bolshakov
  • Elena I. Bolshakova
  • Alexey P. Kotlyarov
  • Alexander Gelbukh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4919)


For extracting collocations from the Internet, it is necessary to numerically estimate the cohesion between potential collocates. Mutual Information cohesion measure (MI) based on numbers of collocate occurring closely together (N 12) and apart (N 1, N 2) is well known, but the Web page statistics deprives MI of its statistical validity. We propose a family of different measures that depend on N 1, N 2 and N 12 in a similar monotonic way and possess the scalability feature of MI. We apply the new criteria for a collection of N 1, N 2, and N 12 obtained from AltaVista for links between a few tens of English nouns and several hundreds of their modifiers taken from Oxford Collocations Dictionary. The ‘noun–its own adjective’ pairs are true collocations and their measure values form one distribution. The ‘noun–alien adjective’ pairs are false collocations and their measure values form another distribution. The discriminating threshold is searched for to minimize the sum of probabilities for errors of two possible types. The resolving power of a criterion is equal to the minimum of the sum. The best criterion delivering minimum minimorum is found.


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  1. 1.
    Bolshakov, I.A., Bolshakova, E.I.: Measurements of Lexico-Syntactic Cohesion by means of Internet. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 790–799. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Bolshakova, E.I., Bolshakov, I.A., Kotlyarov, A.P.: Experiments in Detection and Correction of Russian Malapropisms by means of the Web. International Journal on Information Theories & Applications 12(2), 141–149 (2005)Google Scholar
  3. 3.
    Church, K., Hanks, P.: Word association norms, mutual information, and lexicography. Computational Linguistics 16(1), 22–29 (1990)Google Scholar
  4. 4.
    Evert, S., Krenn, B.: Methods for the qualitative evaluation of lexical association measures. In: Proc. 39th Meeting of the ACL 2001, pp. 188–195 (2001)Google Scholar
  5. 5.
    Wu, H., Zhou, M.: Synonymous Collocation Extraction Using Translation Information,
  6. 6.
    Ikehara, S., Shirai, S., Uchino, H.: A statistical method for extracting uninterrupted and interrupted collocations from very large corpora. In: Proc. COLING 1996 Conference, pp. 574–579 (1996)Google Scholar
  7. 7.
    Keller, F., Lapata, M.: Using the Web to Obtain Frequencies for Unseen Bigram. Computational linguistics 29(3), 459–484 (2003)CrossRefGoogle Scholar
  8. 8.
    Kilgarriff, A., Grefenstette, G.: Introduction to the Special Issue on the Web as Corpus. Computational linguistics 29(3), 333–347 (2003)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Krenn, B., Evert, S.: Can we do better than frequency? A case study on extracting pp-verb collocations. In: Proc. ACL Workshop on Collocations (2001)Google Scholar
  10. 10.
    Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  11. 11.
    Oxford Collocations Dictionary for Students of English. Oxford University Press (2003)Google Scholar
  12. 12.
    Pearce, D.: Synonymy in collocation extraction. In: Proc. Workshop on WordNet and Other Lexical Resources: Applications, Extensions and Customizations. NAACL 2001, Pittsburgh, PA (2001),
  13. 13.
    Xu, R., Lu, Q.: Improving collocation extraction by using syntactic patterns. In: Proc. IEEE Int. Conf. Natural Language Processing and Knowledge Engineering, IEEE NLP-KE apos.05, pp. 52–57 (2005)Google Scholar
  14. 14.
    Seretan, V., Wehrli, E.: Accurate collocation extraction using a multilingual parser. In: Proc. 21st Int. Conf. Computational Linguistics and 44th Annual Meeting of the ACL, Sydney, Australia, pp. 953–960 (2006)Google Scholar
  15. 15.
    Seretan, V., Wehrli, E.: Multilingual collocation extraction: Issues and solutions. In: Proc. Workshop on Multilingual Language Resources and Interoperability, Sydney, Australia, pp. 40–49 (2006)Google Scholar
  16. 16.
    Seretan, V., Nerima, L., Wehrli, E.: A tool for multi-word collocation extraction and visualization in multilingual corpora. In: Proc. 11th EURALEX International Congress EURALEX 2004, Lorient, France, pp. 755–766 (2004)Google Scholar
  17. 17.
    Smadja, F.: Retreiving Collocations from text: Xtract. Computational Linguistics 19(1), 143–177 (1990)Google Scholar
  18. 18.
    Smadja, F.A., McKeown, K.R.: Automatically extracting and representing collocations for language generation. In: Proc. 28th Meeting of the ACL, pp. 252–259 (1990)Google Scholar
  19. 19.
    Wermter, J., Hahn, U.: Collocation Extraction Based on Modifiability Statistics. In: Proc. 20th Int. Conf. Computational Linguistics COLING 2004, pp. 980–986 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Igor A. Bolshakov
    • 1
  • Elena I. Bolshakova
    • 2
  • Alexey P. Kotlyarov
    • 1
  • Alexander Gelbukh
    • 2
  1. 1.Center for Computing Research (CIC)National Polytechnic Institute (IPN)Mexico CityMexico
  2. 2.Faculty of Computational Mathematics and CyberneticsMoscow State Lomonosov UniversityMoscowRussia

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