Abstract
A method for developing a structural model of natural language syntax and semantics is proposed. Factorization of lexical combinability arrays obtained from text corpora generates linguistic databases that are used for analysis of natural language semantics and syntax.
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Van de Cruys, T.: A Non-negative Tensor Factorization Model for Selectional Preference Induction. Journal of Natural Language Engineering 16(4), 417–437 (2010)
Van de Cruys, T., Rimell, L., Poibeau, T., Korhonen, A.: Multi-way Tensor Factorization for Unsupervised Lexical Acquisition. In: Proceedings of COLING 2012, pp. 2703–2720 (2012)
Anisimov, A.V.: Control Space of Syntactic Structures of Natural language. Cybernetics and System Analysis 3, 11–17 (1990)
Klein, D., Manning, C.D.: Accurate Unlexicalized Parsing. In: Proceedings of ACL 2003, pp. 423–430 (2003)
de Marneffe, M.-C., MacCartney, B., Manning, C.D.: Generating Typed Dependency Parses from Phrase Structure Parses. In: Proceedings of LREC (2006), http://nlp.stanford.edu/pubs/LREC06_dependencies.pdf
Lee, D.D., Seung, H.S.: Algorithms for Non-Negative Matrix Factorization. In: NIPS (2000), http://hebb.mit.edu/people/seung/papers/nmfconverge.pdf
Cichocki, A., Zdunek, R., Phan, A.-H., Amari, S.-I.: Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. J. Wiley & Sons, Chichester (2009)
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Anisimov, A., Marchenko, O., Taranukha, V., Vozniuk, T. (2014). Semantic and Syntactic Model of Natural Language Based on Non-negative Matrix and Tensor Factorization. In: Przepiórkowski, A., Ogrodniczuk, M. (eds) Advances in Natural Language Processing. NLP 2014. Lecture Notes in Computer Science(), vol 8686. Springer, Cham. https://doi.org/10.1007/978-3-319-10888-9_18
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DOI: https://doi.org/10.1007/978-3-319-10888-9_18
Publisher Name: Springer, Cham
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