Special issue on technology assisted review systems
Intelligent Systems with Applications, ISSN: 2667-3053, Vol: 20, Page: 200260
2023
- 4Citations
- 5Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
When faced with an information need, an exhaustive search aim to retrieve all the relevant information. The need for comprehensive research is fundamental electronic discovery, systematic reviews, investigation, research in general, and also in the creation of evaluation collections in information retrieval. To support these high-recall retrieval tasks, technology relies on machine learning to develop systems that rank and classify documents with greater efficiency than traditional Boolean keyword searches. Technology-Assisted Review (TAR) is one of the key technologies which can help to improve this process. The aim of this special issue is to investigate the most recent approaches of TAR systems applied in a wide variety of contexts where high recall is necessary: from the classification of emails to the compilation of systematic reviews up to the evaluation of the quality of the assessments.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2667305323000856; http://dx.doi.org/10.1016/j.iswa.2023.200260; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85168324341&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2667305323000856; https://dx.doi.org/10.1016/j.iswa.2023.200260
Elsevier BV
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