17 citations · 30 across the 8 of their papers we have counts for
8 papers
AgAsk: An Agent to Help Answer Farmer's Questions From Scientific Documents
Bevan Koopman, Ahmed Mourad, Hang Li +6
Decisions in agriculture are increasingly data-driven; however, valuable agricultural knowledge is often locked away in free-text reports, manuals and journal articles. Specialised…
MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction
Shuai Wang, Hang Li, Guido Zuccon
Boolean query construction is often critical for medical systematic review literature search. To create an effective Boolean query, systematic review researchers typically spend we…
How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval?
Hang Li, Ahmed Mourad, Bevan Koopman +1
Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation…
To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers
Hang Li, Shuai Wang, Shengyao Zhuang +4
Current pre-trained language model approaches to information retrieval can be broadly divided into two categories: sparse retrievers (to which belong also non-neural approaches suc…
Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach
Shengyao Zhuang, Hang Li, Guido Zuccon
In this paper we study how to effectively exploit implicit feedback in Dense Retrievers (DRs). We consider the specific case in which click data from a historic click log is availa…
MeSH Term Suggestion for Systematic Review Literature Search
Shuai Wang, Hang Li, Harrisen Scells +2
High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for releva…
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study
Hang Li, Shengyao Zhuang, Ahmed Mourad +3
Pseudo-Relevance Feedback (PRF) utilises the relevance signals from the top-k passages from the first round of retrieval to perform a second round of retrieval aiming to improve se…
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls
Hang Li, Ahmed Mourad, Shengyao Zhuang +2
Pseudo Relevance Feedback (PRF) is known to improve the effectiveness of bag-of-words retrievers. At the same time, deep language models have been shown to outperform traditional b…