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:

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:

Topic model evaluation: http://umiacs.umd.edu/~jbg//docs/nips2009-rtl.pdf

Interactive topic modeling:
Topic Models for Categorization:

Janna Layton (she, her)
Administrative Assistant - Product & Technology
Wikimedia Foundation <https://wikimediafoundation.org/>
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