A question answering system in hadith using linguistic knowledge

Abstract

Question answering system aims at retrieving precise information from a large collection of documents. This work presents a question answering method to apply on Hadith in order to provide an informative answer corresponding to the user's query. Hadith englobes stories and qualification of the prophet Muhammad (PBSL). It also includes the sayings of his companions and their disciples. The problem with current methods is that they fail to capture the meaning when comparing a sentence and a user's query; hence there is often a conflict between the extracted sentences and user's requirements. However, our proposed method has successfully tackled this problem through: (1) avoiding extract a passage whose similarity with the query is high but whose meaning is different. (2) Computing the semantic and syntactic similarity of the sentence-to-sentence and sentence-to-query. (3) Expanding the words in both the query and sentences to tackle the fundamental problem of term mismatch between sentences and the user's query. Furthermore, in order to reduce redundant Hadith texts, the proposed method uses the greedy algorithm to impose diversity penalty on the sentences. The experimental results display that the proposed method is able to improve performance compared with the existing methods on Hadith datasets.