FrameNet CNL: A Knowledge Representation and Information Extraction Language

  • Guntis Barzdins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8625)

Abstract

The paper presents a FrameNet-based information extraction and knowledge representation framework, called FrameNet-CNL. The framework is used on natural language documents and represents the extracted knowledge in a tailor-made Frame-ontology from which unambiguous FrameNet-CNL paraphrase text can be generated automatically in multiple languages. This approach brings together the fields of information extraction and CNL, because a source text can be considered belonging to FrameNet-CNL, if information extraction parser produces the correct knowledge representation as a result. We describe a state-of-the-art information extraction parser used by a national news agency and speculate that FrameNet-CNL eventually could shape the natural language subset used for writing the newswire articles.

Keywords

knowledge representation information extraction FrameNet 

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References

  1. 1.
    Fillmore, C.J., Johnson, C.R., Petruck, M.R.L.: Background to FrameNet. International Journal of Lexicography 16, 235–250 (2003)CrossRefGoogle Scholar
  2. 2.
    Baker, C., Ellsworth, M., Erk, K.: SemEval-2007 task 19: Frame semantic structure extraction. In: Proceedings of SemEval-2007: 4th International Workshop on Semantic Evaluations, Prague, pp. 99–104 (2007)Google Scholar
  3. 3.
    Johansson, R., Nugues, P.: LTH: semantic structure extraction using nonprojective dependency trees. In: Proceedings of SemEval-2007: 4th International Workshop on Semantic Evaluations, Prague, pp. 227–230 (2007)Google Scholar
  4. 4.
    Fuchs, N.E., Kaljurand, K., Kuhn, T.: Attempto Controlled English for Knowledge Representation. In: Baroglio, C., Bonatti, P.A., Małuszyński, J., Marchiori, M., Polleres, A., Schaffert, S. (eds.) Reasoning Web. LNCS, vol. 5224, pp. 104–124. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Burchardt, A., et al.: Using FrameNet for the semantic analysis of German: Annotation, representation, and automation. In: Boas, H.C. (ed.) Multilingual FrameNets in Computational Lexicography: Methods and Applications. Mouton de Gruyter, Berlin (2009)Google Scholar
  6. 6.
    Gruzitis, N., Barzdins, G.: Polysemy in Controlled Natural Language Texts. In: Fuchs, N.E. (ed.) CNL 2009 Workshop. LNCS (LNAI), vol. 5972, pp. 102–120. Springer, Heidelberg (2010)Google Scholar
  7. 7.
    Wyner, A., et al.: On Controlled Natural Languages: Properties and Prospects. In: Fuchs, N.E. (ed.) CNL 2009 Workshop. LNCS (LNAI), vol. 5972, pp. 281–289. Springer, Heidelberg (2010)Google Scholar
  8. 8.
    Angelov, K., Ranta, A.: Implementing controlled languages in GF. In: Fuchs, N.E. (ed.) CNL 2009 Workshop. LNCS (LNAI), vol. 5972, pp. 82–101. Springer, Heidelberg (2010)Google Scholar
  9. 9.
    Dannells, D.: Applying semantic frame theory to automate natural language template generation from ontology statements. In: Proceedings of the 6th International Natural Language Generation Conference, pp. 179–183. ACM (2010)Google Scholar
  10. 10.
    Murray, W., Singliar, T.: Spatiotemporal Extensions to a Controlled Natural Language. In: Kuhn, T., Fuchs, N.E. (eds.) CNL 2012 Workshop. LNCS (LNAI), vol. 7427, pp. 61–78. Springer, Heidelberg (2012)Google Scholar
  11. 11.
    Gruzitis, N., Paikens, P., Barzdins, G.: FrameNet Resource Grammar Library for GF. In: Kuhn, T., Fuchs, N.E. (eds.) CNL 2012 Workshop. LNCS (LNAI), vol. 7427, pp. 121–137. Springer, Heidelberg (2012)Google Scholar
  12. 12.
    Daiber, J., Jakob, M., Hokamp, C., Mendes, P.N.: Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th International Conference on Semantic Systems, pp. 121–124. ACM (2013)Google Scholar
  13. 13.
    Wick, M., Singh, S., Pandya, H., McCallum, A.: A Joint Model for Discovering and Linking Entities. In: Proceedings of the 2013 Workshop on Automated Knowledge Base Construction, pp. 67–72. ACM (2013)Google Scholar
  14. 14.
    Das, D., Chen, D., Martins, A.F.T., Schneider, N., Smith, N.A.: Frame-Semantic Parsing. Computational Linguistics 40(1), 9–56 (2014)CrossRefGoogle Scholar
  15. 15.
    Barzdins, G., Gosko, D., Rituma, L., Paikens, P.: Using C5.0 and Exhaustive Search for Boosting Frame-Semantic Parsing Accuracy. In: Proceedings of the 9th Language Resources and Evaluation Conference (LREC), Reykjavik, pp. 4476–4482 (2014)Google Scholar
  16. 16.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)Google Scholar
  17. 17.
    Banarescu, L., Bonial, C., Cai, S., Georgescu, M., Griffitt, K., Hermjakob, U., Knight, K., Koehn, P., Palmer, M., Schneider, N.: Abstract Meaning Representation for Sembanking. In: Proc. Linguistic Annotation Workshop (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Guntis Barzdins
    • 1
  1. 1.Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLatvia

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