57 citations · 422 across the 74 of their papers we have counts for
18 papers · 1 filter
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…
Neural Rankers for Effective Screening Prioritisation in Medical Systematic Review Literature Search
Shuai Wang, Harrisen Scells, Bevan Koopman +1
Medical systematic reviews typically require assessing all the documents retrieved by a search. The reason is two-fold: the task aims for ``total recall''; and documents retrieved…
Dependency-aware Self-training for Entity Alignment
Bing Liu, Tiancheng Lan, Wen Hua +1
Entity Alignment (EA), which aims to detect entity mappings (i.e. equivalent entity pairs) in different Knowledge Graphs (KGs), is critical for KG fusion. Neural EA methods dominat…
Guiding Neural Entity Alignment with Compatibility
Bing Liu, Harrisen Scells, Wen Hua +3
Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs). While numerous neural EA models have been devised, they are mainly learned using labelled…
Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search
Shuai Wang, Harrisen Scells, Bevan Koopman +1
High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for releva…
High-quality Task Division for Large-scale Entity Alignment
Bing Liu, Wen Hua, Guido Zuccon +2
Entity Alignment (EA) aims to match equivalent entities that refer to the same real-world objects and is a key step for Knowledge Graph (KG) fusion. Most neural EA models cannot be…
Rethinking Persistent Homology for Visual Recognition
Ekaterina Khramtsova, Guido Zuccon, Xi Wang +1
Persistent topological properties of an image serve as an additional descriptor providing an insight that might not be discovered by traditional neural networks. The existing resea…
Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation
Shengyao Zhuang, Houxing Ren, Linjun Shou +4
The Differentiable Search Index (DSI) is an emerging paradigm for information retrieval. Unlike traditional retrieval architectures where index and retrieval are two different and…
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…
Is Non-IID Data a Threat in Federated Online Learning to Rank?
Shuyi Wang, Guido Zuccon
In this perspective paper we study the effect of non independent and identically distributed (non-IID) data on federated online learning to rank (FOLTR) and chart directions for fu…
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…
CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos
Shengyao Zhuang, Guido Zuccon
Current dense retrievers are not robust to out-of-domain and outlier queries, i.e. their effectiveness on these queries is much poorer than what one would expect. In this paper, we…
From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search
Shuai Wang, Harrisen Scells, Justin Clark +2
Medical systematic review query formulation is a highly complex task done by trained information specialists. Complexity comes from the reliance on lengthy Boolean queries, which e…
Case law retrieval: problems, methods, challenges and evaluations in the last 20 years
Daniel Locke, Guido Zuccon
Case law retrieval is the retrieval of judicial decisions relevant to a legal question. Case law retrieval comprises a significant amount of a lawyer's time, and is important to en…
Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training
Shengyao Zhuang, Guido Zuccon
The process of model checkpoint validation refers to the evaluation of the performance of a model checkpoint executed on a held-out portion of the training data while learning the…
Reinforcement Online Learning to Rank with Unbiased Reward Shaping
Shengyao Zhuang, Zhihao Qiao, Guido Zuccon
Online learning to rank (OLTR) aims to learn a ranker directly from implicit feedback derived from users' interactions, such as clicks. Clicks however are a biased signal: specific…