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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-91947-8_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91946-1
Online ISBN: 978-3-319-91947-8
eBook Packages: Computer ScienceComputer Science (R0)