Thanks Leila. I just want to note that although I can't be present during
the meeting tomorrow, I'm keenly interested in the VE research and will
look into using lessons from that research to benefit the video project
that's currently incubating as an IEG draft [1].
Regards,
Pine
[1]
On Mon, Jul 27, 2015 at 2:47 PM, Leila Zia <leila(a)wikimedia.org> wrote:
Hi everyone,
The next Research showcase will be live-streamed this Wednesday, July 29
at 11.30 PT. The streaming link will be posted on the lists a few minutes
before the showcase starts (sorry, we haven't been able to solve this, yet.
:-() and as usual, you can join the conversation on IRC at #wikimedia
-research.
We look forward to seeing you!
Leila
This month:
*VisualEditor's effect on newly registered users*By *Aaron Halfaker*
<https://www.mediawiki.org/wiki/User:Halfak_%28WMF%29>
It's been nearly two years since we ran an initial study
<https://meta.wikimedia.org/wiki/Research:VisualEditor%27s_effect_on_newly_registered_editors/June_2013_study>
of VisualEditor's effect on newly registered editors. While most of the
results of this study were positive (e.g. workload on Wikipedians did not
increase), we still saw a significant decrease in the newcomer
productivity. In the meantime, the Editing
<https://www.mediawiki.org/wiki/Editing> team has made substantial
improvements to performance and functionality. In this presentation, I'll
report on the results of a new experiment designed to test the effects of
enabling this improved VisualEditor software for newly registered users
by default. I'll show what we learned from the experiment and discuss some
results have opened larger questions about what, exactly, is difficult
about being a newcomer to English Wikipedia.
*Wikipedia knowledge graph with DeepDive*
By *Juhana Kangaspunta* and
*Thomas Palomares (10-week student project)*
Despite the tremendous amount of information present on Wikipedia, only a
very little amount is structured. Most of the information is embedded in
text and extracting it is a non-trivial challenge. In this project, we try
to populate Wikidata, a structured component of Wikipedia, using DeepDive
tool to extract relations embedded in the text. We finally extracted more
than 140,000 relations with more than 90% average precision. We will
present DeepDive and the data that we use for this project, we explain
the relations we focused on so far and explain the implementation and
pipeline, including our model, features and extractors. Finally, we detail
our results with a thorough precision and recall analysis.
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