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First Steps in Automatic Anaphora Resolution in Lithuanian Language Based on Morphological Annotations and Named Entity Recognition

  • Voldemaras Žitkus
  • Lina NemuraitėEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 538)

Abstract

Anaphora resolution is an important part of natural language processing used in machine translation, semantic search and various other information retrieval and understanding systems. Anaphora resolution algorithms usually require linguistic pre-processing tools and various expensive resources for automatically identifying anaphoric expressions. Many smaller languages, like Lithuanian, lack such resources and tools. In this paper, an algorithm is proposed that requires only morphological annotations and recognized named entities. The paper presents experimental results showing the relevance of the solution for specific domains, and considers the further immediate ways towards dealing with the overall anaphora resolution problem for Lithuanian language.

Keywords

Anaphora resolution Natural language processing Named entity recognition NER Lithuanian language References 

References

  1. 1.
    Mitkov, R.: Anaphora Resolution. Longman, London (2002)zbMATHGoogle Scholar
  2. 2.
    Elango, P.: Coreference resolution: a survey. Technical report, University of Wisconsin-Madison, USA (2005)Google Scholar
  3. 3.
    Mitkov, R., Lappin, S., Boguraev, B.: Introduction to the special issue on computational anaphora resolution. Comput. Linguist. 27(4), 473–477 (2001)CrossRefGoogle Scholar
  4. 4.
    SemantikaLT: Syntactic-semantic analysis and search system for lithuanian internet, corpus and public sector applications (2012–2014), no VP2-3.1-IVPK-12-K (2014)Google Scholar
  5. 5.
    OMG: Semantics of Business Vocabulary and Business Rules (SBVR). SBVR 1.2, version 1.2, OMG Document Number: formal/2013-11-04, pp. 1–292 (2012)Google Scholar
  6. 6.
    Sukys, A., Nemuraite, L., Sinkevicius, E., Paradauskas, B.: Querying ontologies on the base of semantics of business vocabularies and business rules. In: Information Technologies’ 2011: Proceedings of the 17th International Conference on Information and Software Technologies, IT 2011, pp. 247–254, Kaunas, Lithuania, 27–29 April 2011Google Scholar
  7. 7.
    Sukys, A., Nemuraite, L., Paradauskas, B.: Representing and transforming SBVR question patterns into SPARQL. In: Skersys, T., Butleris, R., Butkiene, R. (eds.) ICIST 2012. CCIS, vol. 319, pp. 436–451. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Bernotaityte, G., Nemuraite, L., Butkiene, R., Paradauskas, B.: Developing SBVR vocabularies and business rules from OWL2 ontologies. In: Skersys, T., Butleris, R., Butkiene, R. (eds.) ICIST 2013. CCIS, vol. 403, pp. 134–145. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Karpovic, J., Krisciuniene, G., Ablonskis, L., Nemuraite, L.: The comprehensive mapping of semantics of business vocabulary and business rules (SBVR) to OWL 2 ontologies. Inf. Technol. Contr. 43(3), 289–302 (2014)Google Scholar
  10. 10.
    Zitkus, V., Nemuraite, L.: Taxonomy of anaphoric expressions as a starting point for anaphora resolution in Lithuanian corpus. In: IVUS 2014, pp. 177–182, Lithuania (2014)Google Scholar
  11. 11.
    Hobbs, J.R.: Resolving pronoun references. In: Grosz, B., Sparck-Jones, K., Webber, B. (eds.) Reading in Natural Language Processing, vol. 99, pp. 339–352. Morgan Kaufmann Publishers Inc., San Francisco (1986)Google Scholar
  12. 12.
    Tetrault, J.R.: A corpus-based evaluation of centering and pronoun resolution. Comput. Linguis. 27(4), 507–520 (2001)CrossRefGoogle Scholar
  13. 13.
    Byron, D. K.: Resolving pronominal references to abstract entities. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 80–87, Philadelphia, USA (2002)Google Scholar
  14. 14.
    Lappin, S., Leass, H.J.: An algorithm for pronominal anaphora resolution. Comput. Linguis. 20(4), 535–561 (1994)Google Scholar
  15. 15.
    Ge, N., Hale, J., Charniak, E.: A statistical approach to anaphora resolution. In: Proceedings of the Sixth Workshop of Very Large Corpora, pp. 161–170 (1998)Google Scholar
  16. 16.
    Soon, W.M., Ng, H.T., Lim, D.C.Y.: A machine learning approach to coreference resolution of noun phrases. Comput. Linguis. 27(4), 521–544 (2001)CrossRefGoogle Scholar
  17. 17.
    Balaji, J., Geetha, T.V., Parthasarathi, R., Karky, M.: Anaphora resolution in Tamil using universal networking language. In: Proceedings of the Indian International Conference on Artificial Intelligence, IICAI-2011, Karnataka, India (2011)Google Scholar
  18. 18.
    Fischer, W.: Linguistically motivated ontology-based information retrieval. Doctoral dissertation, University of Augsburg, GER (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Information SystemsKaunas University of TechnologyKaunasLithuania

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