Analysis of the quality of academic papers by the words in abstracts
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 10274 LNCS, Page: 434-443
2017
- 4Captures
Metric Options: CountsSelecting 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.
Metrics Details
- Captures4
- Readers4
Conference Paper Description
The investigation of related research is very important for research activities. However, it is not easy to choose an appropriate and important academic paper from among the huge number of possible papers. The researcher searches by combining keywords and then selects an paper to be checked because it uses an index that can be evaluated. The citation count is commonly used as this index, but information about recently published papers cannot be obtained. This research attempted to identify good papers using only the words included in the abstract. We constructed a classifier by machine learning and evaluated it using cross validation. As a result, it was found that a certain degree of discrimination is possible.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85025146149&origin=inward; http://dx.doi.org/10.1007/978-3-319-58524-6_34; https://link.springer.com/10.1007/978-3-319-58524-6_34; https://link.springer.com/content/pdf/10.1007/978-3-319-58524-6_34; https://dx.doi.org/10.1007/978-3-319-58524-6_34; https://link.springer.com/chapter/10.1007/978-3-319-58524-6_34
Springer Science and Business Media LLC
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