Enhancing the Performance of Semantic Search in Bengali using Neural Net and other Classification Techniques
Arijit Das1, Diganta Saha2
1Arijit Das, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Jadavpur University, Kolkata, West Bengal.
2*Diganta Saha, Professor of the Department of Computer Science and Engineering in Jadavpur University, Kolkata, West Bengal.
Manuscript received on January 20, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 29, 2020. | PP: 4170-4176 | Volume-9 Issue-3, February 2020. | Retrieval Number: B3566129219/2020©BEIESP | DOI: 10.35940/ijeat.B3566.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: To know the information from the internet searching is one of the most important part for any user. In case of ‘Syntactic Search’ keyword based matching technique is used. Search accuracy is improved applying the filter like location, preference, user-history etc. However, it can happen that the user query or question and the best available answer or result in the internet domain has no terms in common or ignorable number of terms is common. In such case syntactic search cannot give the desired output. The role of ‘Semantic Search’ becomes prevalent in this scenario. The execution of semantic search faces challenge due to unavailability of resources like WordNet, Ontology, Annotation etc. An end to end algorithm is described to improve the accuracy of the semantic search in this work. Four classification techniques are used. They are ANN, Decision Tree, SVM and Naïve Bayes. Dataset is provided from the TDIL project of the Ministry of Electronics and IT, Govt. of India. The repository contains 86 categories of text having more than a million sentences. After getting the impressive result for the Bengali language test run was done for other Indian languages and a very good result is achieved. This research is extremely useful for the automatic question answering system, semantic similarity analysis, e-governance and m- governance.
Keywords: Semantics, ANN, SVM, Naive Bayes, Decision Tree, Classification Techniques, Semantic Search.