Zobrazeno 1 - 10
of 155
pro vyhledávání: '"Knoth, Petr"'
Systematic literature reviews (SLRs) play an essential role in summarising, synthesising and validating scientific evidence. In recent years, there has been a growing interest in using machine learning techniques to automate the identification of rel
Externí odkaz:
http://arxiv.org/abs/2311.12474
Keeping up with research and finding related work is still a time-consuming task for academics. Researchers sift through thousands of studies to identify a few relevant ones. Automation techniques can help by increasing the efficiency and effectivene
Externí odkaz:
http://arxiv.org/abs/2309.01684
In this paper, we present CORE-GPT, a novel question-answering platform that combines GPT-based language models and more than 32 million full-text open access scientific articles from CORE. We first demonstrate that GPT3.5 and GPT4 cannot be relied u
Externí odkaz:
http://arxiv.org/abs/2307.04683
Clinical trials (CTs) often fail due to inadequate patient recruitment. This paper tackles the challenges of CT retrieval by presenting an approach that addresses the patient-to-trials paradigm. Our approach involves two key components in a pipeline-
Externí odkaz:
http://arxiv.org/abs/2307.00381
Current methods of evaluating search strategies and automated citation screening for systematic literature reviews typically rely on counting the number of relevant and not relevant publications. This established practice, however, does not accuratel
Externí odkaz:
http://arxiv.org/abs/2306.17614
Autor:
Thelwall, Mike, Kousha, Kayvan, Abdoli, Mahshid, Stuart, Emma, Makita, Meiko, Wilson, Paul, Levitt, Jonathan, Knoth, Petr, Cancellieri, Matteo
Publikováno v:
Quantitative Science Studies, 4(2), 547-573 (2023)
National research evaluation initiatives and incentive schemes have previously chosen between simplistic quantitative indicators and time-consuming peer review, sometimes supported by bibliometrics. Here we assess whether artificial intelligence (AI)
Externí odkaz:
http://arxiv.org/abs/2212.05415
Confidence estimation of classification based on the distribution of the neural network output layer
One of the most common problems preventing the application of prediction models in the real world is lack of generalization: The accuracy of models, measured in the benchmark does repeat itself on future data, e.g. in the settings of real business. T
Externí odkaz:
http://arxiv.org/abs/2210.07745
In the process of Systematic Literature Review, citation screening is estimated to be one of the most time-consuming steps. Multiple approaches to automate it using various machine learning techniques have been proposed. The first research papers tha
Externí odkaz:
http://arxiv.org/abs/2201.07534
Recent years have seen fast growth in the number of policies mandating Open Access (OA) to research outputs. We conduct a large-scale analysis of over 800 thousand papers from repositories around the world published over a period of 5 years to invest
Externí odkaz:
http://arxiv.org/abs/1906.03307
Publikováno v:
In Intelligent Systems with Applications May 2023 18