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 …

The rapid advancement of artificial intelligence (AI) has spotlighted ChatGPT as a key
technology in the realm of information retrieval (IR). Unlike its predecessors, it offers notable …

Existing pseudo-relevance feedback (PRF) methods often divide an original query into
individual terms for processing and select expansion terms based on the term frequency …

In the domain of information retrieval (IR), the matching of query and document relies on
ranking models to calculate the degree of their relevance. Therefore, ranking models remain …

Purpose The objective of the manuscript is to propose a hybrid algorithm combining the
improved BM25 algorithm, k-means clustering, and BioBert model to better determine …

Recently, recurrent neural networks (RNN) have achieved great success in the aspect-
based sentiment classification task. Existing approaches always focus on capture the local …

In this article, we propose a Latent Dirichlet Allocation–(LDA) based topic-graph probabilistic
personalization model for Web search. This model represents a user graph in a latent topic …

Recently, large pretrained language models (PLMs) have led a revolution in the information
retrieval community. In most PLMs‐based retrieval frameworks, the ranking performance …

Pre-trained models have garnered significant attention in the field of information retrieval,
particularly for improving document ranking. Typically, an initial retrieval step using sparse …

Traditional pseudo relevance feedback (PRF) models choose top k feedback documents for
query expansion and treat those documents equally. When k is determined, feedback terms …