Improving Information Retrieval Results for Persian Documents using FarsNet
This work addresses the challenge of enhancing search results for Persian-language users, but it is incremental as it applies an existing query expansion technique to a specific domain.
The paper tackled the problem of improving information retrieval for Persian documents by using FarsNet for query expansion, resulting in a 9% improvement in Mean Average Precision compared to a baseline without query expansion.
In this paper, we propose a new method for query expansion, which uses FarsNet (Persian WordNet) to find similar tokens related to the query and expand the semantic meaning of the query. For this purpose, we use synonymy relations in FarsNet and extract the related synonyms to query words. This algorithm is used to enhance information retrieval systems and improve search results. The overall evaluation of this system in comparison to the baseline method (without using query expansion) shows an improvement of about 9 percent in Mean Average Precision (MAP).