Thanks, Janna . I'm forwarding this to a few more lists.
Pine
(
https://meta.wikimedia.org/wiki/User:Pine )
---------- Forwarded message ---------
From: Janna Layton <jlayton(a)wikimedia.org>
Date: Thu, Mar 12, 2020 at 7:30 PM
Subject: [Analytics] [Wikimedia Research Showcase] March 18, 2020:
Topic Modeling
To: <wiki-research-l(a)lists.wikimedia.org>rg>,
<analytics(a)lists.wikimedia.org>rg>, <wikimedia-l(a)lists.wikimedia.org>
Hi all,
The next Research Showcase will be live-streamed on Wednesday, March
18, at 9:30 AM PDT/16:30 UTC. We’ll have a presentation on topic
modeling by Jordan Boyd-Graber. A question-and-answer session will
follow.
YouTube stream:
https://www.youtube.com/watch?v=fiD9QTHNVVM
As usual, you can join the conversation on IRC at #wikimedia-research.
You can also watch our past research showcases here:
https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
This month's presentation:
Big Data Analysis with Topic Models: Evaluation, Interaction, and
Multilingual Extensions
By: Jordan Boyd-Graber, University of Maryland
A common information need is to understand large, unstructured
datasets: millions of e-mails during e-discovery, a decade worth of
science correspondence, or a day's tweets. In the last decade, topic
models have become a common tool for navigating such datasets even
across languages. This talk investigates the foundational research
that allows successful tools for these data exploration tasks: how to
know when you have an effective model of the dataset; how to correct
bad models; how to measure topic model effectiveness; and how to
detect framing and spin using these techniques. After introducing
topic models, I argue why traditional measures of topic model
quality---borrowed from machine learning---are inconsistent with how
topic models are actually used. In response, I describe interactive
topic modeling, a technique that enables users to impart their
insights and preferences to models in a principled, interactive way. I
will then address measuring topic model effectiveness in real-world
tasks.
Overview of topic models:
https://mimno.infosci.cornell.edu/papers/2017_fntir_tm_applications.pdf
Topic model evaluation:
http://umiacs.umd.edu/~jbg//docs/nips2009-rtl.pdf
Interactive topic modeling:
http://umiacs.umd.edu/~jbg//docs/2014_mlj_itm.pdf
Topic Models for Categorization:
http://users.umiacs.umd.edu/~jbg//docs/2016_acl_doclabel.pdf
--
Janna Layton (she, her)
Administrative Assistant - Product & Technology
Wikimedia Foundation
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