  Despite the advantages of their low-resource settings, traditional sparse
retrievers depend on exact matching approaches between high-dimensional
bag-of-words (BoW) representations of both the queries and the collection. As a
result, retrieval performance is restricted by semantic discrepancies and
vocabulary gaps. On the other hand, transformer-based dense retrievers
introduce significant improvements in information retrieval tasks by exploiting
low-dimensional contextualized representations of the corpus. While dense
retrievers are known for their relative effectiveness, they suffer from lower
efficiency and lack of generalization issues, when compared to sparse
retrievers. For a lightweight retrieval task, high computational resources and
time consumption are major barriers encouraging the renunciation of dense
models despite potential gains. In this work, we propose boosting the
performance of sparse retrievers by expanding both the queries and the
documents with linked entities in two formats for the entity names: 1) explicit
and 2) hashed. We employ a zero-shot end-to-end dense entity linking system for
entity recognition and disambiguation to augment the corpus. By leveraging the
advanced entity linking methods, we believe that the effectiveness gap between
sparse and dense retrievers can be narrowed. We conduct our experiments on the
MS MARCO passage dataset. Since we are concerned with the early stage retrieval
in cascaded ranking architectures of large information retrieval systems, we
evaluate our results using recall@1000. Our approach is also capable of
retrieving documents for query subsets judged to be particularly difficult in
prior work. We further demonstrate that the non-expanded and the expanded runs
with both explicit and hashed entities retrieve complementary results.
Consequently, we adopt a run fusion approach to maximize the benefits of entity
linking.
