Abstract
The article considers developing an effective flexible model for describing syntactic structures of natural language. The model of an augmented transition network in the automaton form is chosen as a basis. This automaton performs the sentence analysis algorithm using forward error detection and backward error correction passes. The automaton finds an optimal variant of error corrections using a technique similar to the Viterbi decoding algorithm for error correction convolution codes. As a result, an effective tool for natural language parsing is developed.
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References
Szanser, A.J.: Automatic error-correction in natural languages. Inf. Storage Retr. 5(4), 169–174 (1970)
Riseman, E.M., Hanson, A.R.: A contextual postprocessing system for error correction using binary n-grams. IEEE Trans. Comput. C–23(5), 480–493 (1974)
MacDonald, N.H., Frase, L.T., Gingrich, P.S., Keenan, S.A.: The Writer’s workbench: computer aids for text analysis. IEEE Trans. Commun. 30(1), 105–110 (1982)
Jensen, K., Heidorn, G.E., Miller, L.A., Ravin, Y.: Parse fitting and prose fixing: getting a hold on ill-formedness. Comput. Linguist. Spec. Issue Ill-formed Input 9(3–4), 147–160 (1983)
Richardson, S., Braden-Harder, L.: The Experience of Developing a Large-Scale Natural Language Processing System: Critique. In: Jensen, K., Heidorn, G.E., Richardson, S.D. (eds.) Natural Language Processing: The PLNLP Approach. The Kluwer International Series in Engineering and Computer Science (Natural Language Processing and Machine Translation), pp. 77–89. Springer, Boston (1993). https://doi.org/10.1007/978-1-4615-3170-8_7
Adriaens, G.: Simplified English grammar and style correction in an MT framework: the LRE SECC project. Aslib Proc. 47(3), 73–82 (1995)
Sun, C., Jin, X., Lin, L., Zhao, Y., Wang, X.: Convolutional neural networks for correcting english article errors. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds.) NLPCC 2015. LNCS (LNAI), vol. 9362, pp. 102–110. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25207-0_9
Yuan, Z. and Briscoe, T.: Grammatical error correction using neural machine translation. In: Proceedings of NAACL-HLT 2016, pp. 380–386 (2016)
Woods, W.: Transition network grammars for natural language analysis. Commun. ACM 13(10), 591–606 (1970)
Feldman, J., Abou-Faycal, I., Frigo, M.: A fast maximum-likelihood decoder for convolutional codes. In: Vehicular Technology Conference, pp. 371–375 (2002)
Dahlmeier, D., Ng, H.T., Wu, S.M.: Building a large annotated corpus of learner english: the NUS Corpus of Learner English. In: Proceedings of the 8th Workshop on Innovative Use of NLP for Building Educational Applications, pp. 22–31 (2013)
Dale, R., Kilgarriff, A.: Helping our own: the HOO 2011 pilot shared task. In: Proceedings of the 13th European Workshop on Natural Language Generation, pp. 242–249 (2011)
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Marchenko, O., Anisimov, A., Zavadskyi, I., Melnikov, E. (2018). English Text Parsing by Means of Error Correcting Automaton. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_28
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DOI: https://doi.org/10.1007/978-3-319-91947-8_28
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