[Colloquium] REMINDER: Talks at TTIC: Brendan O'Connor, Carnegie Mellon University

[Dawn Ellis]

When:     Monday, January 27th at 10am

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room #526

Who:       Brendan O'Connor, Carnegie Mellon University

TITLE:

Statistical Text Analysis for Social Science

ABSTRACT:

What can text analysis tell us about society?  Corpora of news, books, and
social media encode human beliefs and culture. But it is impossible for a
researcher to read all of today's rapidly growing text archives.  My
research develops statistical text analysis methods that measure social
phenomena from textual content, especially in news and social media data.
 For example: How do changes to public opinion appear in microblogs?  What
topics get censored in the Chinese Internet?  What character archetypes
recur in movie plots?  How do geography and ethnicity affect the diffusion
of new language?  In order to answer these questions effectively, we must
apply and develop scientific methods in statistics, computation, and
linguistics.

In this talk I will illustrate these methods in a project that analyzes
events in international politics.  Political scientists are interested in
studying international relations through *event data*: time series records
of who did what to whom, as described in news articles.  To address this
event extraction problem, we develop an unsupervised Bayesian model of
semantic event classes, which learns the verbs and textual descriptions
that correspond to types of diplomatic and military interactions between
countries.  The model uses dynamic logistic normal priors to drive the
learning of semantic classes; but unlike a topic model, it leverages deeper
linguistic analysis of syntactic argument structure.  Using a corpus of
several million news articles over 15 years, we quantitatively evaluate how
well its event types match ones defined by experts in previous work, and
how well its inferences about countries correspond to real-world conflict.
 The method also supports exploratory analysis; for example, of the recent
history of Israeli-Palestinian relations.

BIO:

Brendan O'Connor (http://brenocon.com/) is a 5th year Ph.D. candidate in
Carnegie Mellon University's Machine Learning Department.  He is interested
in statistical machine learning and natural language processing, especially
when informed by or applied to the social sciences.  In the past he has
been a Visiting Fellow at the Harvard Institute for Quantitative Social
Science, an intern in the Facebook Data Science group, and has worked on
crowdsourcing (Crowdflower/Dolores Labs) and "semantic" search (Powerset).
 His undergraduate degree was Symbolic Systems.

Host: Kevin Gimpel, kgimpel at ttic.edu


-- 
*Dawn Ellis*
Administrative Coordinator,
Bookkeeper
773-834-1757
dellis at ttic.edu

TTIC
6045 S. Kenwood Ave.
Chicago, IL. 60637
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