ANNOYED-SECTIONAL CLASSIFICATION OF THE ACTUAL CLASSIFICATION OF SENSITIVE INCIDENTS

Chittaluri Vivek Kumar, V Sridhar Reddy

Abstract


We order a list of frequently checking queries to clear the query issues and implement the method known as the results of the QDMiner engine. Most clearly, the QDMiner lists the quotes and HTML tags free of charge inside your search engine top, calling out the results of the corresponding groups of these products from them, and subsequently categorizing and categorizing groups and products based on the menu access and better product. Our proposed method is a source and does not depend on any type of domain understanding. The main purpose of the mining regions is different from the suggestions for questions. We recommend resolving the administrator, that Once to QDMiner, in order to issue a quick search for removing and integrating regular text and HTML tags and domains repeatedly within the best search engine results. Also check out two lists, and find out that the right issues can be found by using the exact similarity between lists and punctuation lists. The results of the test indicate that there are plenty of available fields and cannot be found useful for the QDMiner question. Our proposed method is general and does not depend on any specific location. As a result, she is able to answer open-domain questions. Depending on the questions. Instead of constantly relying on your concerns, we improve the details of the high papers received by the query.


References


I. Szpektor, A. Gionis, and Y. Maarek, “Improving recommendation for long-tail queries via templates,” in Proc. 20th Int. Conf. World Wide Web, 2011, pp. 47–56.

J. Pound, S. Paparizos, and P. Tsaparas, “Facet discovery for structured web search: A query-log mining approach,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2011, pp. 169–180.

O. Etzioni, M. Cafarella, D. Downey, S. Kok, A.-M. Popescu, T. Shaked, S. Soderland, D. S. Weld, and A. Yates, “Web-scale information extraction in knowitall: (preliminary results),” in Proc. 13th Int. Conf. World Wide Web, 2004, pp. 100–110.

Y. Liu, R. Song, M. Zhang, Z. Dou, T. Yamamoto, M. P. Kato, H. Ohshima, and K. Zhou, “Overview of the NTCIR-11 imine task,” in Proc. NTCIR-11, 2014, pp. 8–23.

Zhicheng Dou, Member, IEEE, Zhengbao Jiang, Sha Hu, Ji-Rong Wen, and Ruihua Song, “Automatically Mining Facets for Queriesfrom Their Search Results”, ieee transactions on knowledge and data engineering, vol. 28, no. 2, february 2016.

A. Herdagdelen, M. Ciaramita, D. Mahler, M. Holmqvist, K. Hall, S. Riezler, and E. Alfonseca, “Generalized syntactic and semantic models of query reformulation,” in Proc. 33rd Int. ACM SIGIR Conf. Res. Develop. Inf. retrieval, 2010, pp. 283–290.


Full Text: PDF

Refbacks

  • There are currently no refbacks.




Copyright © 2012 - 2023, All rights reserved.| ijitr.com

Creative Commons License
International Journal of Innovative Technology and Research is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJITR , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.