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Using deep learning for short text understanding

Overview of attention for article published in Journal of Big Data, October 2017
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 X users

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
60 Mendeley
Title
Using deep learning for short text understanding
Published in
Journal of Big Data, October 2017
DOI 10.1186/s40537-017-0095-2
Authors

Justin Zhan, Binay Dahal

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 23%
Student > Ph. D. Student 11 18%
Student > Doctoral Student 6 10%
Student > Bachelor 4 7%
Student > Postgraduate 3 5%
Other 9 15%
Unknown 13 22%
Readers by discipline Count As %
Computer Science 27 45%
Mathematics 3 5%
Engineering 3 5%
Agricultural and Biological Sciences 2 3%
Business, Management and Accounting 2 3%
Other 7 12%
Unknown 16 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 October 2017.
All research outputs
#14,829,499
of 23,006,268 outputs
Outputs from Journal of Big Data
#193
of 339 outputs
Outputs of similar age
#191,578
of 327,882 outputs
Outputs of similar age from Journal of Big Data
#5
of 9 outputs
Altmetric has tracked 23,006,268 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 339 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.3. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
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 327,882 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.