Improving Arabic Microblog Retrieval with Distributed Representations

Abstract

Query expansion (QE) using pseudo relevance feedback (PRF) is one of the approaches that has been shown to be effective for improving microblog retrieval. In this paper, we investigate the performance of three different embedding-based methods on Arabic microblog retrieval: Embedding-based QE, Embedding-based PRF, and PRF incorporated with embedding-based reranking. Our experimental results over three variants of EveTAR test collection showed a consistent improvement of the reranking method over the traditional PRF baseline using both MAP and P@10 evaluation measures. The improvement is statistically-significant in some cases. However, while the embedding-based QE fails to improve over the traditional PRF, the embedding-based PRF successfully outperforms the baseline in several cases, with a statistically-significant improvement using MAP measure over two variants of the test collection.