An optimal method for enhancing the generation of machine code from natural language data set

  • Authors

    • chhayarani Ram Kinkar sati
    • Yogendra Kumar Jain SATI
    2019-12-15
    https://doi.org/10.14419/ijet.v8i4.26593
  • Numerical Value, Utility Based Objectives, Constrained Based Decomposition, Structured Data Set.
  • Natural language processing is a very active area of research and development, there is not a single agreed upon a method that would satisfy everyone for the use of natural language to operate electronic devices or other practical applications. But there are some aspects used from many years in the formulation and solution of computational problem arising in natural language processing. This paper describes a model in which numerical values are assigned to word of natural language speech data set to convert the information present in natural language speech data set into an intermediate numeric form as a structured data set. The intermediated numerical values of each word will be used for generation of machine code which will be easily understand by electronic devices to draw inferences from data set. The designed model is useful for a number of practical applications and very simple to implement.

     

     

  • References

    1. [1] T.Sainath,B.Kingsbury,H.Soltau,andB.Ramabhadran,“Optimization techniques to improve training speech of deep belief networks for large speech tasks,â€IEEETrans.Audio,Speech,Lang.Process.,Spec.Iss. Nov. 2013. vol. 21 pp 2331-2342. https://doi.org/10.1109/TASL.2013.2284378.

      [2] Stephen J. Wright, Dimitri Kanevsky, Li Deng , Xiaodong He “Optimization Algorithms and Applications for Speech and Language Processing†IEEE Trans. Audio, Speech, Lang. Process., Nov. 2013, vol. 21, no. 11, pp. 2231–2242. https://doi.org/10.1109/TASL.2013.2283777.

      [3] Victor m sanchez , jon antnio†A generalised alignment template formalism and its application to the inference of shallow-transfer machine translation rules from scarce bilingual corpora†computer speech and language,july 2015,vol 32,issue.1 pp 46-90. https://doi.org/10.1016/j.csl.2014.10.003.

      [4] V.Hautamäki,K.A.Lee,T.Kinnunen,B.Ma,andH.Li,“Optimizing the performance of spoken language recognition with discriminative training,†IEEE Trans. Audio, Speech, Lang. Process., Aug. 2013, vol. 21, no. 8, pp. 1622–1631.

      [5] Rico Sennrich Philip,Willams mathias huck†A tree does not make a well-formed sentence: Improving syntactic string-to-tree statistical machine translation with more linguistic knowledge†computer speech and language ,july 2015 vol 32,issue.1pp 27-45. https://doi.org/10.1016/j.csl.2014.09.002.

      [6] Li Deng, Xiao Li, “Machine Learning Paradigms for Speech Recognition: An Overview†IEEE Trans. Audio, Speech, Lang. Process., May. 2013, vol. 21, no. 5, pp. 1060–1089. https://doi.org/10.1109/TASL.2013.2244083.

      [7] Steven Bird, Ewan Klein, Edward loper “Natural Language Processing in Python†OREILLY.

      [8] Magnus Lie Hetdmol “Python Algorithm†APRESS.

      [9] Marta R Costa- Jussa and jose A R Fonollosa†Latest trends in hybrid machine translation and its applications†computer speech and language ,july 2015, vol 32,issue.1pp 3-10. https://doi.org/10.1016/j.csl.2014.11.001.

      [10] Antonio Toral, Pavel Pecina, Josefvan genabith “Linguistically-augmented perplexity-based data selection for language models†computer speech and language ,july 2015.,vol 32,issue.1pp 11-26. https://doi.org/10.1016/j.csl.2014.10.002.

      [11] Mirjam Sepesy MauÄec, Gregor Donaj†Morphology In Statistical Machine Translation From English To Highly Inflectional Language†Journal of information technology and control 2018,vol 47 no.1 pp 63-74. https://doi.org/10.5755/j01.itc.47.1.17887.

      [12] Tetsuo Sawaragi, Sosuke Iwai, Osamu Katai ,â€A Human-Friendly Interface System for Decision Support Based on Self-Organized Multi-Layered Knowledge Structures†Toward Interactive and Intelligent Decision Support Systems, Volume 286 of the series Lecture Notes in Economics and Mathematical Systems pp 30-39. https://doi.org/10.1007/978-3-642-46609-0_4.

      [13] Eva Hasler, Adrià de Gispert, Felix Stahlberg, AurelienWaite, Bill Byrne†Source sentence simplification for statistical machine translation†computer speech and language, sept.2017, vol 45,pp 221-235. https://doi.org/10.1016/j.csl.2016.12.001.

      [14] Wright, S.J. Kanevsky, D. ; Li Deng ; Xiaodong He†Optimization Algorithms and Applications for Speech and Language Processing “Audio, Speech, and Language Processing, IEEE Transaction 2013 ,Volume:21,Issue: 8. https://doi.org/10.1109/TASL.2013.2283777.

      [15] Felipe Sánchez-Martínez.â€Choosing the best machine translation system to translate a sentence by using only source-language informationâ€. In Proceedings of the 15th Annual Conference of the European Associtation for Machine Translation, May 2011.

  • Downloads

  • How to Cite

    Ram Kinkar, chhayarani, & Kumar Jain, Y. (2019). An optimal method for enhancing the generation of machine code from natural language data set. International Journal of Engineering & Technology, 8(4), 590-595. https://doi.org/10.14419/ijet.v8i4.26593