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Linguistic Habit Graphs Used for Text Representation and Correction

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Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10246))

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Abstract

This paper introduces a novel associative way of storing, compressing, and processing sentences. The Linguistic Habit Graphs (LHG) are introduced as graph models that could be used for spell checking, text correction, proof–reading, and compression of sentences. All the above mentioned functionalities are always available in the constant computational complexity as a result of the associative way of text processing, special kinds of connections and graph nodes that enable to activate various important relations between letters and words simultaneously for any given contexts. Furthermore, using the proposed graph structure, new algorithms have been developed to provide effective text analyzes and contextual text correction. These new algorithms can properly locate and often automatically correct typical mistakes in texts written in a given language for which the graph was build.

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References

  1. 1000x Faster Spelling Correction algorithm, June 2012. http://blog.faroo.com/2012/06/07/improved-edit-distance-based-spelling-correction/. Online; Visited 03 Oct 2016

  2. Brown, P.F., et al.: Class-based n-gram models of natural language. Comput. Linguist. 18(4), 467–479 (1992)

    Google Scholar 

  3. Blunsom, P.: Hidden Markov Models. Lecture notes, vol. 15, pp. 18–19, August 2004

    Google Scholar 

  4. Chen, S.F., Goodman, J.: An empirical study of smoothing techniques for language modeling. In: Proceedings of the 34th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics (1996)

    Google Scholar 

  5. Ide, N., Pustejovsky, J. (eds.): Handbook of Linguistic Annotation. Springer, Netherlands (2017). doi:10.1007/978-94-024-0881-2

    Google Scholar 

  6. Deepak, P., Deshpande, P.M.: Operators for Similarity Search. Semantics, Techniques and Usage Scenarios. Springer International Publishing, Cham (2015)

    Google Scholar 

  7. Gadamer, M., Horzyk, A.: Text analysis and correction using specialized linguistic habit graphs LHG. Image Process. Commun. 17(4), 245–250 (2012). Bydgoszcz

    Google Scholar 

  8. Horzyk, A.: Artificial Associative Systems and Associative Artificial Intelligence, pp. 1–280. Academic Publishing House EXIT, Warsaw (2013)

    Google Scholar 

  9. Horzyk, A.: How does generalization and creativity come into being in neural associative systems and how does it form human-like knowledge? Neurocomputing 144, 238–257 (2014). Elsevier

    Google Scholar 

  10. Horzyk, A.: Innovative types and abilities of neural networks based on associative mechanisms and a new associative model of neurons. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9119, pp. 26–38. Springer, Cham (2015). doi:10.1007/978-3-319-19324-3_3

    Chapter  Google Scholar 

  11. Horzyk, A., Gadamer, M.: Associative text representation and correction. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS (LNAI), vol. 7894, pp. 76–87. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38658-9_7

    Chapter  Google Scholar 

  12. How to write a spelling corrector. http://norvig.com/spell-correct.html. Online; Visited: 05 Oct 2016

  13. Jurafsky, D.: Speech & language processing. Pearson Education India, Noida (2000)

    Google Scholar 

  14. Kohonen, T., Somervuo, P.: Self-organizing maps of symbol strings. Neurocomputing 21(1), 19–30 (1998)

    Article  MATH  Google Scholar 

  15. Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing, vol. 999. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  16. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: ACL (System Demonstrations), pp. 55–60 (2014)

    Google Scholar 

  17. Martin, D., Jurafsky, J.H., Jurafsky, D.: Speech and Language Processing. An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall, Upper Saddle River (2000)

    Google Scholar 

  18. Savary, A., Piskorski, J.: Lexicons and Grammars For Named Entity Annotation in the National Corpus of Polish, pp. 141–154. Intelligent Information Systems, Siedlce (2010)

    Google Scholar 

  19. Stolcke, A.: SRILM-an extensible language modeling toolkit. In: Proceedings International Conference on Spoken Language Processing, pp. 257–286 (2002)

    Google Scholar 

  20. Täckström, O., Das, D., Petrov, S., McDonald, R., Nivre, J.: Token and type constraints for cross-lingual part-of-speech tagging. Trans. Assoc. Comput. Linguist. 1, 1–12 (2013)

    Google Scholar 

  21. The soundex indexing system. https://goo.gl/5BUcR5 (May 2007). Online; Visited 04 Oct 2016

  22. What are some algorithms of spelling correction that were used by search engine? https://goo.gl/8xhvpQ. Online; Visited 03 Oct 2016

  23. Whitelaw, C., Hutchinson, B., Chung, G.Y., Ellis, G.: Using the web for language independent spellchecking and autocorrection. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 2, pp. 890–899. Association for Computational Linguistics (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza Av. 30, 30–059, Krakow, Poland

    Marcin Gadamer

Authors
  1. Marcin Gadamer
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Corresponding author

Correspondence to Marcin Gadamer .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Gadamer, M. (2017). Linguistic Habit Graphs Used for Text Representation and Correction. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_22

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  • DOI: https://doi.org/10.1007/978-3-319-59060-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59059-2

  • Online ISBN: 978-3-319-59060-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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