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A Study on Deep Linguistic Processing with Special Reference to Semantic and Syntactic Levels

by Partha Sarkar, Bipul Syam Purkayastha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 88 - Number 13
Year of Publication: 2014
Authors: Partha Sarkar, Bipul Syam Purkayastha
10.5120/15410-3872

Partha Sarkar, Bipul Syam Purkayastha . A Study on Deep Linguistic Processing with Special Reference to Semantic and Syntactic Levels. International Journal of Computer Applications. 88, 13 ( February 2014), 6-9. DOI=10.5120/15410-3872

@article{ 10.5120/15410-3872,
author = { Partha Sarkar, Bipul Syam Purkayastha },
title = { A Study on Deep Linguistic Processing with Special Reference to Semantic and Syntactic Levels },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 88 },
number = { 13 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 6-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume88/number13/15410-3872/ },
doi = { 10.5120/15410-3872 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:07:29.963394+05:30
%A Partha Sarkar
%A Bipul Syam Purkayastha
%T A Study on Deep Linguistic Processing with Special Reference to Semantic and Syntactic Levels
%J International Journal of Computer Applications
%@ 0975-8887
%V 88
%N 13
%P 6-9
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Natural Language Processing (NLP) sets a relation between human and computer where the elements of human language, be it spoken or written, are organized so that a computer can perform tasks accordingly based on their interaction. The goal of the Natural Language Processing (NLP) is to design and make software that will help to analyze, understand, and generate languages that humans use naturally, so that in the long run we will be able to address our computer according to our convenience. This goal is not easy to reach because the natural language, the symbol system, that is easiest for humans to learn and use, is hardest for a computer to master and interpret in a meaningful way. Though machines today are capable of inverting large matrix with speed and grace, they still fail to master the basics of our spoken and written languages. The obvious problems arise from the semantic and syntactic ambiguities which in most of the cases becomes difficult to present through a software programme. As an English speaker we can effortlessly understand a sentence like "My mind is flying in joy". But this sentence presents difficulties to a software program that lacks both our knowledge of the world and our experience with linguistic structures. Deep Linguistic Processing, in this connection, is an important area of study to achieve this goal.

References
  1. Andreas, Steve, and Faulkner, Charles eds. February 19, 1999 "NLP: The New Technology of Achievement" William Morrow Paperbacks.
  2. D. W. Aha, D. Kibler, and M. K. Albert, 1991 "Instance-based Learning Algorithms: Machine Learning", ISSN 0885-6125.
  3. C. Apt´e, F. Damerau, and S. M. Weiss, 1994 "Automated Learning of Decision Rules for Text Categorization", ACM Transactions on Information Systems, ISSN 1046-8188.
  4. E. Charniak, 1996 "Tree-bank Grammars", Technical report, Department of Computer Science, Brown University.
  5. E. Charniak, 1997 "Statistical Parsing with a Context-free Grammar and Word Statistics", Proceedings of the Fourteenth National Conference on Artificial Intelligence.
  6. M. Collins, 2003 "Head-driven Statistical Models for Natural Language Parsing", Computational Linguitics.
  7. M. Davy and S. Luz, Dec. 2008 "An Adaptive Pre-filtering Technique for Error-reduction Sampling in Active Learning", International Conference on Data Mining Workshops, pp 682–691, IEEE Press, Pisa.
  8. G. Escudero, L. M`arquez, and G. Rigau, 2000 "Boosting Applied to Word Sense Disambiguation", R. L. D. M´antaras and E. Plaza, eds, Proceedings of ECML-00, 11th European Conference on Machine Learning, pp 129–141, Barcelona, Springer Verlag.
  9. G. Forman, 2003 "An Extensive Empirical Study of Feature Selection Metrics for Text Classification", Journal of Machine Learning Research, ISSN 1533-7928.
  10. M. Haruno, S. Shirai, and Y. Ooyama, 1999 "Using Decision Trees to Construct a Practical Parser", Machine Learning, pp. 131–149.
  11. Palmer, Stone, Martha. February 13, 2006 "Semantic Processing for Finite Domains (Studies in Natural Language Processing)" 1st ed. Cambridge University Press.
  12. T. Joachims, 1998 "Text Categorization with Support Vector Machines: Learning with Many Relevant Features", in Proceedings of ECML-98, 10th European Conference on Machine Learning, pp. 137–142, Chemnitz.
  13. Vladimir. A. Fomichov, December 4, 2009 "Semantics-Oriented Natural Language Processing: Mathematical Models and Algorithms", Springer.
  14. Vaknin, Shlomo. July 25, 2009 "NLP For Beginners: Only The Essentials", Inner Patch Publishing.
Index Terms

Computer Science
Information Sciences

Keywords

Ambiguities Deep Linguistic Processing Interaction Symbol-system Semantic Syntactic

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