IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
SCImago Journal & Country Rank

Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 9 - Issue 4, April 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Extracting Text Features Using Learning-To-Rank-Methods From The Perspective Of Information Retrieval

[Full Text]

 

AUTHOR(S)

YellepeddiVijayalakshmi, B. Arun Kumar, G. K. D. PrasannaVenkatesan

 

KEYWORDS

Information retrieval, Ranking, Text parsing, visual content, textual content

 

ABSTRACT

In recent times, retrieving relevant information from a huge amount of data has gained the attention of researchers. Diverse search systems are offered for this purpose; however, it should have the ability to attain the most appropriate search outcomes in accordance withuser query that fulfils user needs. Various techniques are also provided to retrieve information. Generally, in conventional search engines, text content is considered and images in the content may be violated. However, images in web pages are utilized for retrieving other appropriate images by evaluating their visual and textual content. Also, in conventional text-based search engines, appropriate images are retrieved with visual features by providing a textual query. Diverse search engines and systems are presented for easy access and retrieval of relevant multi-media content on a ranking basis. This ranking approach is based on textual content that is phrased from huge data with visual contents. So, this study provides an effectual ranking approach based on Text parsing from Multi-Source document (R-TPM) producing information retrieval by eliminating redundancy. Simulation is carried out in MATLAB environment; the proposed model shows better trade-off in contract with the existing IR approaches based on ranking.

 

REFERENCES

[1] Y.Cao, J.Xu, T.Y.Liu, H. Li,Y. Huang, and H.W.Hon, “Adapt-ing Ranking SVM to Document Retrieval,”In Proceedings of the 29th annual international ACM SIGIR conference on re-search and development in information retrieval (pp. 186-193). ACM, 2006.
[2] T.Y. Liu,”Learning to Rank for Information Retrieval,”Founda-tions and Trends in Information Retrieval, 3(3), 225-331, 2009.
[3] Q. Wu,C.J.Burges, K.M. Svore,and J.Gao, “Ranking, Boost-ing, and Model Adaptation,”TecnicalReport, MSR-TR-2008-109, 2008.
[4] X. Yin, J.X.Huang, Z.Li, and X.Zhou, “A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia,”IEEE Transactions onKnowledge and Data Engi-neering, 25(6), 1201-1212, 2013.
[5] Y. Chen, X.Yin, Z.Li, X.Hu, and J.X.Huang, “Promoting Rank-ing Diversity for Biomedical Information Retrieval based on LDA,”In Bioinformatics and Biomedicine (BIBM), 2011 IEEE International Conference on (pp. 456-461). IEEE, 2011.
[6] X. An, and J.X.Huang, “Boosting Novelty for Biomedical In-formation Retrieval Through Probabilistic Latent Semantic Analysis,”In Proceedings of the 36th international ACM SIGIR conference on Research and development in information re-trieval (pp. 829-832). ACM, 2013.
[7] J. Sun, S.Wang, B.J. Gao, and J.Ma, “Learning to Rank for Hy-brid Recommendation,”In Proceedings of the 21st ACM inter-national conference on Information and knowledge manage-ment (pp. 2239-2242). ACM, 2012.
[8] Lin, H.Lin, Z.Ye, S.Jin, and X.Sun, “Learning to Rank with Groups,”In Proceedings of the 19th ACM international confer-ence on Information and knowledge management (pp. 1589-1592). ACM, 2010.
[9] Vargas, P. Castells, andD. Vallet, “Explicit RelevanceModels in Intent-Oriented Information Retrieval Diversification,”In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (pp. 75-84). ACM, 2012.
[10] C. J. Burges, ``From ranknet to lambdarank to lambdamart: An overview,'' Learning, vol. 11, p. 81, Jun. 2010.
[11] C. J. Burges, R. Ragno, and Q. V. Le, ``Learning to rank with nonsmooth cost functions,'' in Proc. Adv. Neural Inf. Process. Syst., 2007, pp. 193_200.
[12] Y. Kim, ``Convolutional neural networks for sentence classi_cation,'' in Proc. Empirical Methods Natural Lang. Process., 2014, pp. 1746_1751.
[13] X. Cheng, Y. Chen, B. Cheng, S. Li, and G. Zhou, ``An emotion cause corpus for chinese microblogs with multiple-user structures,'' ACM Trans. Asian Low-Resource Lang. Inf. Process., vol. 17, no. 1, p. 6, 2017.
[14] L. Gui, D. Wu, R. Xu, Q. Lu, and Y. Zhou, ``Event-driven emotion cause extraction with corpus construction,'' in Proc. Conf. Empirical Methods Natural Lang. Process., 2016, pp. 1639_1649.
[15] L. Gui, J. Hu, Y. He, R. Xu, Q. Lu, and J. Du, ``A question answering approach to emotion cause extraction,'' in Proc. Conf. Empirical MethodsNatural Lang. Process., 2017, pp. 1593_1602.
[16] Rajendran T et al, “Recent Innovations in Soft Computing Applications”, Current Signal Transduction Therapy, Vol. 14, No. 2, pp. 129 – 130, 2019.
[17] Emayavaramban G et al, “Indentifying User Suitability in sEMG based Hand Prosthesis for using Neural Networks”, Current Signal Transduction Therapy, Vol. 14, No. 2, pp. 158 – 164, 2019.
[18] Rajendran T & Sridhar K P, “Epileptic seizure classification using feed forward neural network based on parametric features”. International Journal of Pharmaceutical Research, 10(4): 189-196, 2018.
[19] Hariraj V et al, “Fuzzy multi-layer SVM classification of breast cancer mammogram images”, International Journal of Mechanical Engineering and Technology, Vol. 9, No.8, pp. 1281-1299, 2018.
[20] Muthu F et al, “Design of CMOS 8-bit parallel adder energy efficient structure using SR-CPL logic style”, Pakistan Journal of Biotechnology, Vol. 14, No. Special Issue II, pp. 257-260, 2017.
[21] Yuvaraj P et al, “Design of 4-bit multiplexer using Sub-Threshold Adiabatic Logic (STAL)”, Pakistan Journal of Biotechnology, Vol. 14, No. Special Issue II, pp. 261-264, 2017.
[22] Keerthivasan S et al, “Design of low intricate 10-bit current steering digital to analog converter circuitry using full swing GDI”, Pakistan Journal of Biotechnology, Vol. 14, No. Special Issue II, pp. 204-208, 2017.
[23] Vijayakumar P et al, “Efficient implementation of decoder using modified soft decoding algorithm in Golay (24, 12) code”, Pakistan Journal of Biotechnology, Vol. 14, No. Special Issue II, pp. 200-203, 2017.
[24] Rajendran T & Sridhar K P, “Epileptic Seizure-Classification using Probabilistic Neural Network based on Parametric Features”, International Journal of Scientific & Technological Research, Vol.9, No. 3, 2020 (Accepted for Publication).
[25] Rajendran T et al, “Performance analysis of fuzzy multilayer support vector machine for epileptic seizure disorder classification using auto regression features”, Open Biomedical Engineering Journal, Vol. 13, pp. 103-113, 2019.
[26] Rajendran T et al, “Advanced algorithms for medical image processing”, Open Biomedical Engineering Journal, Vol. 13, 102, 2019.
[27] Anitha T et al, “Brain-computer interface for persons with motor disabilities - A review”, Open Biomedical Engineering Journal, Vol. 13, pp. 127-133, 2019.