Word2Vec-GloVe-BERT Embeddings for Query Expansion

Query expansion represents a crucial task and a decisive step in many information retrieval applications such as document retrieval and question answering. Its success has a major impact on the performance of the subsequent steps. In this paper, we present a global corpus based-query expansion method. This method relies on the one hand on the WordNet knowledge resource to expand documents. On the other hand, it rests on pre-trained embedding models Word2Vec, GloVe and BERT to identify and select the relevant expansion embedding terms. Our main motivation is to combine the knowledge contained in structured and unstructured semantic resources by exploiting the advantage of introducing deep learning which seeks to bring out the semantic relationships buried in large corpus of documents. The experimental results in the context of document retrieval process showed that our method performs well.