  Recent years have witnessed a substantial increase in the use of deep
learning to solve various natural language processing (NLP) problems. Early
deep learning models were constrained by their sequential or unidirectional
nature, such that they struggled to capture the contextual relationships across
text inputs. The introduction of bidirectional encoder representations from
transformers (BERT) leads to a robust encoder for the transformer model that
can understand the broader context and deliver state-of-the-art performance
across various NLP tasks. This has inspired researchers and practitioners to
apply BERT to practical problems, such as information retrieval (IR). A survey
that focuses on a comprehensive analysis of prevalent approaches that apply
pretrained transformer encoders like BERT to IR can thus be useful for academia
and the industry. In light of this, we revisit a variety of BERT-based methods
in this survey, cover a wide range of techniques of IR, and group them into six
high-level categories: (i) handling long documents, (ii) integrating semantic
information, (iii) balancing effectiveness and efficiency, (iv) predicting the
weights of terms, (v) query expansion, and (vi) document expansion. We also
provide links to resources, including datasets and toolkits, for BERT-based IR
systems. A key highlight of our survey is the comparison between BERT's
encoder-based models and the latest generative Large Language Models (LLMs),
such as ChatGPT, which rely on decoders. Despite the popularity of LLMs, we
find that for specific tasks, finely tuned BERT encoders still outperform, and
at a lower deployment cost. Finally, we summarize the comprehensive outcomes of
the survey and suggest directions for future research in the area.
