<![CDATA[In this work, a legal document retrieval system is presented that estimates the significance of the user queries to appropriate legal sub-domains and extracts the key documents containing required information quickly. In order to develop such a system, a document repository is prepared comprising the documents and case study reports of different Indian legal matters of last five years. A legal sub-domain classification technique using deep neural network (DNN) model is used to obtain the relevance of the user queries with respective legal sub-domains for quick information retrieval. A query-document relevance (QDR) score-based technique is presented to rank the output documents in relation to the query terms. The presented model is evaluated by performing several experiments under different context and the performance of the presented model is analyzed. The presented model achieves an average precision score of 0.98 and recall score of 0.97 in the experiments performed. The retrieval model is assessed with other retrieval models and the presented model achieves 13% and 12% increase average accuracy with respect to precision scores and recall measures respectively compared to the traditional models showing the strength of the presented model.]]>
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