Search Results for author:
Found 13 papers, 9 papers with code
Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Cross-encoders are effective passage re-rankers.
Zero-shot Generative Large Language Models for Systematic Review Screening Automation
Systematic reviews are crucial for evidence-based medicine as they comprehensively analyse published research findings on specific questions.
Evaluating Generative Ad Hoc Information Retrieval
Recent advances in large language models have enabled the development of viable generative information retrieval systems.
Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation
Our best approach is not only viable based on the information available at the time of screening, but also has similar effectiveness to the final title.
Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models
As in other fields of artificial intelligence, the information retrieval community has grown interested in investigating the power consumption associated with neural models, particularly models of search.
The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives
The Archive Query Log (AQL) is a previously unused, comprehensive query log collected at the Internet Archive over the last 25 years.
Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?
The ability of ChatGPT to follow complex instructions and generate queries with high precision makes it a valuable tool for researchers conducting systematic reviews, particularly for rapid reviews where time is a constraint and often trading-off higher precision for lower recall is acceptable.
Neural Rankers for Effective Screening Prioritisation in Medical Systematic Review Literature Search
An empirical analysis compares how effective neural methods compare to traditional methods for this task.
Guiding Neural Entity Alignment with Compatibility
Making compatible predictions thus should be one of the goals of training an EA model along with fitting the labelled data: this aspect however is neglected in current methods.
Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search
However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query.
From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search
However, we show pseudo seed studies are not representative of real seed studies used by information specialists.
Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study
Our results also indicate that our reproduced screening prioritisation method, (1) is generalisable across datasets of similar and different topicality compared to the original implementation, (2) that when using multiple seed studies, the effectiveness of the method increases using our techniques to enable this, (3) and that the use of multiple seed studies produces more stable rankings compared to single seed studies.
ActiveEA: Active Learning for Neural Entity Alignment
Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion.
