DSSM with text hashing technique for text document retrieval in next generation search engine for big data and data analytics
Digital world is coming, were data as become big data with ever increase in large volume of digital information available in terms of text documents. This tends for data extraction, enrichment, analysis and retrieval of text documents which are in the form of unstructured nature becomes a major problem in search engine. Traditionally text documents are the source of storing our information; either personal or professional. Today text documents are generating at very high speed, and need to be process the data on time to upgrade the search engine. It is also important for organizations including private and public which have been collecting large volume of domain specific text document information, which may contain national intelligence, education, medical information, business and marketing. In this paper we present a system that enriches the information retrieval process of text documents in search engine from unstructured data and bringing the big data and data analytics world into educational sector and make the best of both worlds by using the latest cutting edge technology deep structured semantic modeling with text hashing and proposing a next generation search engine.
2016 IEEE International Conference on Engineering and Technology (ICETECH)