Multi-word similarity and retrieval model for a refined retrieval of quranic sentences

Abstract

This paper addresses the task of learning sentence similarity on pairs of relevant sentences retrieved from a Quranic Retrieval Application (QRA). With the existing keywords and semantic concepts extraction, a long list of relevant verses (sentences) is retrieved that matches the query. However, as Quranic concepts are repeatedly conveyed on scattered sentences, it is important to classify which of the retrieved sentences are similar not only in word function but in context with subsequence words. Information context on similar sentences is realized with the evaluation of both word similarity and relatedness. This paper proposed a multi-word Term Similarity and Retrieval (mTSR) model that uses the n-gram score function that measures the relatedness of subsequent words. Bigram similarity scores are constructed between every pair of the relevant Quranic sentences, which boost the conventional keyword matched QRA. A similarity score is established to refine the list of relevant sentences aimed to help the user to understand the scattered content of the documents. The results are presented to the user as a refined list of similar sentences, by ranking the first-retrieved results from a keyword search. The ranking is done using a bigram score. When the score is tested on the Malay Quranic Retrieval Application (myQRA) prototype, results show that the refined results accurately matched the manually perceived similar sentences (iS) extracted by the three volunteers.