Social Networks With Natural Learning Processing- Employability of Key Influencers Lexical, Syntactic and Semantic in Enhancing Business Intelligence

Karan Gupta

Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana

Download PDF

Abstract

References

1. Chowdhury, G. G. (2003). Natural language processing. Yearly evaluation of ist, 37(1), 51-89.
2. De Kleer, J. (1986). An assumption-based TMS. AI, 28(2), 127-162
3. Turing, A. M. (2009). Calculating equipment and aptitude. In Analyzing the Turing Test (pp. 23-65). Springer, Dordrecht
4. Goldberg, D. E., & Holland, J. H. (1988). GA and ml. Ml, 3(2), 95-99
5. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning.Nature, 521(7553), 436
6. Rayner, K., & Duffy, S. A. (1986). Verbal difficulty and obsession periods in analysis: Belongings of term occurrence, verb difficulty, and verbal uncertainty. Retention & reasoning, 14(3), 191-201.
7. MacDonald, M. C., Pearlmutter, N. J., & Seidenberg, M. S. (1994). The verbal environment of syntactic uncertainty resolve. Mental evaluation, 101(4), 676.
8. Maes, S. H., & Neti, C. V. (2005). U.S. Patent No. 6,964,023. Washington, DC: U.S.Patent and Trademark Office
9. Lenhart, A., & Madden, M. (2007).Social networking websites and teens: An overview
10. Hengeveld, K. (1992). Parts of speech. Covered arrangement and position in a practical viewpoint, 29-55
11. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by buried semantic examination. Paper of the American humanity for info science, 41(6), 391-407
12. Attardo, S. (2010). Humorous texts: A semantic and practical examination (Vol. 6). Walter de Gruyter

Back