A friendly reminder that this is happening in 23 min. :-) YouTube stream: https://www.youtube.com/watch?v=vGyrVg_qKSM IRC: #wikimedia-research
Best, Leila 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
