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] https://meta.wikimedia.org/wiki/Grants:IEG/Motivational_and_educational_video_to_introduce_Wikimedia On Mon, Jul 27, 2015 at 2:47 PM, Leila Zia <[email protected]> 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. > > _______________________________________________ > Wiki-research-l mailing list > [email protected] > https://lists.wikimedia.org/mailman/listinfo/wiki-research-l > >
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