Zobrazeno 1 - 10
of 654
pro vyhledávání: '"Stevenson, Mark P."'
The fusion of multi-source data is essential for a comprehensive analysis of geographic applications. Due to distinct data structures, the fusion process tends to encounter technical difficulties in terms of preservation of the intactness of each sou
Externí odkaz:
http://arxiv.org/abs/2407.10599
Autor:
Leidner, Jochen L., Stevenson, Mark
Over the course of the recent decade, tremendous progress has been made in the areas of machine learning and natural language processing, which opened up vast areas of potential application use cases, including hiring and human resource management. W
Externí odkaz:
http://arxiv.org/abs/2405.07766
Autor:
Bin-Hezam, Reem, Stevenson, Mark
We present RLStop, a novel Technology Assisted Review (TAR) stopping rule based on reinforcement learning that helps minimise the number of documents that need to be manually reviewed within TAR applications. RLStop is trained on example rankings usi
Externí odkaz:
http://arxiv.org/abs/2405.02525
The Document Set Expansion (DSE) task involves identifying relevant documents from large collections based on a limited set of example documents. Previous research has highlighted Positive and Unlabeled (PU) learning as a promising approach for this
Externí odkaz:
http://arxiv.org/abs/2403.17473
Document set expansion aims to identify relevant documents from a large collection based on a small set of documents that are on a fine-grained topic. Previous work shows that PU learning is a promising method for this task. However, some serious iss
Externí odkaz:
http://arxiv.org/abs/2401.11145
Autor:
Bin-Hezam, Reem, Stevenson, Mark
Technology Assisted Review (TAR) stopping rules aim to reduce the cost of manually assessing documents for relevance by minimising the number of documents that need to be examined to ensure a desired level of recall. This paper extends an effective s
Externí odkaz:
http://arxiv.org/abs/2312.03171
Autor:
Stevenson, Mark, Bin-Hezam, Reem
Technology Assisted Review (TAR), which aims to reduce the effort required to screen collections of documents for relevance, is used to develop systematic reviews of medical evidence and identify documents that must be disclosed in response to legal
Externí odkaz:
http://arxiv.org/abs/2311.08597
Autor:
Wanyan, Xinye, Seneviratne, Sachith, Nice, Kerry, Thompson, Jason, White, Marcus, Langenheim, Nano, Stevenson, Mark
Footpath mapping, modeling, and analysis can provide important geospatial insights to many fields of study, including transport, health, environment and urban planning. The availability of robust Geographic Information System (GIS) layers can benefit
Externí odkaz:
http://arxiv.org/abs/2309.09446
Autor:
Robinson, Ambrose, Thorne, William, Wu, Ben P., Pandor, Abdullah, Essat, Munira, Stevenson, Mark, Song, Xingyi
Medical systematic reviews can be very costly and resource intensive. We explore how Large Language Models (LLMs) can support and be trained to perform literature screening when provided with a detailed set of selection criteria. Specifically, we ins
Externí odkaz:
http://arxiv.org/abs/2308.06610
Although it has been demonstrated that Natural Language Processing (NLP) algorithms are vulnerable to deliberate attacks, the question of whether such weaknesses can lead to software security threats is under-explored. To bridge this gap, we conducte
Externí odkaz:
http://arxiv.org/abs/2211.15363