Question Answering Passage Retrieval and Re-ranking Using N-grams and SVM

Abstract

Over the last few decades, with the meteoric rise of Information Technology, Question Answering (QA) has attracted more attention and has been extremely explored. Indeed, several QA systems are based on a passage retrieval engine which aims to deliver a set of passages that are most likely to contain a relevant response to a question stated in natural language. In an attempt to enhance the performance of existing QASs by increasing the number of generated correct answers and ensure their relevance, we propose a novel approach for retrieving and re-ranking passages based on n-grams and SVM models. The core principle is to first rely on the dependency degree of n-gram words of the query in the passage to retrieve correct passages. Then, an SVM based model is used to improve passage ranking incorporating various lexical, syntactic and semantic similarity measures. Emperical evaluation performed with the CLEF dataset demonstrates the merits of our approach: the results obtained by our implemented system transcend that of other previously proposed ones.