shipra goyal goyal, Dhavleesh Rattan


This paper expects to investigate the part of regular dialect preparing (NLP). The paper will examine the part with regards to mechanized information recovery, robotized question answer, and content organizing. NLP systems are increasing more extensive acknowledgment, in actuality, applications and modern concerns. There are different complexities required in preparing the content of regular dialect that could fulfill the need of leaders. This paper starts with the portrayal of the characteristics of NLP practices. The paper then spotlights on the difficulties in normal dialect preparing. The paper additionally talks about significant systems of NLP. The last segment portrays open doors and difficulties for future examination.


Natural Language Processing, Lexical, Syntactic, Morphology

Full Text:



Choi. 1999a. An adaptive voting mechanism for improving the reliability of natural lan- guage processing systems. Paper submitted to EACL'99, January.

Collobert, R., & Weston, J. (2007). Fast semantic extrac- tion using a novel neural network architecture. Proceed- ings of the 45th Annual Meeting of the ACL (pp. 560– 567).

Ueffing, N., Haffari, G., & Sarkar, A. (2007). Transductive learning for statistical machine translation. Proceedings of the 45th Annual Meeting of the ACL, 25–32.

Rosenfeld, B., & Feldman, R. (2007). Using Corpus Statis- tics on Entities to Improve Semi-supervised Relation Ex- traction from the Web. Proceedings of the 45th Annual Meeting of the ACL, 600–607. Marneffe, B. MacCartney, C. D. Manning, “Generating Typed Dependency Parses from Phrase Structure Parses”, In Proceedings of the IEEE /ACL 2006 Workshop on Spoken Language Technology. The Stanford Natural Language Processing Group. 2006

Marie-Catherine de Marneffe, Bill MacCartney, Christopher D. Manning,“Generating Typed Dependency Parses from Phrase Structure Parses”, In LREC 2006.



  • There are currently no refbacks.

Copyright (c) 2017 International Journal of Advanced Research in Computer Science