57 citations · 422 across the 74 of their papers we have counts for
7 papers · 1 filter
Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study
Shuai Wang, Harrisen Scells, Ahmed Mourad +1
Screening or assessing studies is critical to the quality and outcomes of a systematic review. Typically, a Boolean query retrieves the set of studies to screen. As the set of stud…
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…
ActiveEA: Active Learning for Neural Entity Alignment
Bing Liu, Harrisen Scells, Guido Zuccon +2
Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods -- neural EA model…
Dealing with Typos for BERT-based Passage Retrieval and Ranking
Shengyao Zhuang, Guido Zuccon
Passage retrieval and ranking is a key task in open-domain question answering and information retrieval. Current effective approaches mostly rely on pre-trained deep language model…
Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion
Shengyao Zhuang, Guido Zuccon
BERT-based information retrieval models are expensive, in both time (query latency) and computational resources (energy, hardware cost), making many of these models impractical esp…
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…