Pseudo Relevance Feedback Technique and Semantic Similarity for Corpus-based Expansion

Abstract

The adaptation of a Query Expansion (QE) approach for Arabic documents may produce the worst rankings or irrelevant results. Therefore, we have introduced a technique, which is to utilise the Arabic WordNet in the corpus and query expansion level. A Point-wise Mutual Information (PMI) corpus-based measure is used to semantically select synonyms from the WordNet. In addition, Automatic Query Expansion (AQE) and Pseudo Relevance Feedback (PRF) methods were also explored to improve the performance of the Arabic information retrieval (AIR) system. The experimental results of our proposed techniques for AIR shows that the use of Arabic WordNet in the corpus and query level together with AQE, and the adaptation of PMI in the expansion process have successfully reduced the level of ambiguity as these techniques select the most appropriate synonym. It enhanced knowledge discovery by taking care of the relevancy aspect. The techniques also demonstrated an improvement in Mean Average Precision by 49%, with an increase of 7.3% in recall in comparison to the baseline.