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A Semi-automatic Error Retrieval Method for Uncovering Collocation Errors from a Large Learner Corpus

Overview of attention for article published in English Teaching & Learning, November 2019
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Title
A Semi-automatic Error Retrieval Method for Uncovering Collocation Errors from a Large Learner Corpus
Published in
English Teaching & Learning, November 2019
DOI 10.1007/s42321-019-00037-y
Authors

Christine Ting-Yu Yang, Howard Hao-Jan Chen, Chen-Yu Liu, Yu-Cheng Liu

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Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 December 2019.
All research outputs
#12,452,449
of 14,083,335 outputs
Outputs from English Teaching & Learning
#17
of 38 outputs
Outputs of similar age
#214,869
of 262,500 outputs
Outputs of similar age from English Teaching & Learning
#3
of 6 outputs
Altmetric has tracked 14,083,335 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 38 research outputs from this source. They receive a mean Attention Score of 0.6. This one scored the same or higher as 21 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 262,500 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.