Abstract
Finding similarity degree is one of the significant technologies used in the sample-based machine translation. It works in the following principle, first matching the input sentences with a sentence in the sample database, after that it is necessary to pick up parts of the similar sentences for the sentence which is aimed to translate; it is finished by correcting the structure or paraphrasing it with a relevant meaning. For that reason, the degree of similarity of two samples highly affects on the results of translation. Thus, there are dependence between quality of the outputs and the similarity degree.
The original version of this chapter was revised: In the initially published contribution the affiliations for some of the authors were stated incorrectly. The erratum to this chapter is available at DOI: 10.1007/978-3-319-47674-2_38
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-47674-2_38
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Kamanur, U., Sharipbay, A., Altenbek, G., Bekmanova, G., Zhetkenbay, L. (2016). Investigation and Use of Methods for Defining the Extends of Similarity of Kazakh Language Sentences. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_14
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DOI: https://doi.org/10.1007/978-3-319-47674-2_14
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