Have you read about our latest research on assessing error rates when assigning gender to a name?
Here's the story! https://t.co/AaIeBXW2Ne
From our published paper in @PeerJCompSci https://t.co/XaseSfO1Ge
The study shows that women are underrepresented. We notice the small, manually annotated data sample. It'd be extremely interesting to run an analysis on a larger data set, maybe spanning several decades.
Automatic gender assignments would then be needed: https://t.co/XaseSfwqOG
via @RichardEudes - Comparison and benchmark of name-to-gender inference services https://t.co/MGhmvM4GH9 #datascience, #statistics https://t.co/sfGgcifZtK
via @RichardEudes - Comparison and benchmark of name-to-gender inference services https://t.co/wM7KM76IPa #datascience, #statistics https://t.co/midCYVzP5z
via @RichardEudes - Comparison and benchmark of name-to-gender inference services https://t.co/kpriI7RAum #datascience, #statistics https://t.co/it9dPIOXGX
via @RichardEudes - Comparison and benchmark of name-to-gender inference services https://t.co/zz2M5N4QvD #datascience, #statistics https://t.co/T8oRVVY6jH
Our article "Comparison and benchmark of name-to-gender inference services", published on @PeerJCompSci, evaluates how accurately algorithms can predict the gender of a name.
Take a look: https://t.co/3MO5pgv4Gy
#datascience #benchmark #gender #algorithms
How well can an algorithm detect whether a person's name is male or female?
We have tried and tested some name-to-gender services to tell you!
--> https://t.co/XaseSfwqOG
#research #gender #datascience