Natural language research announcements

[Ming-Wei Chang <mchang21 AT uiuc.edu>]

Dear faculty and students,

   It is our pleasure to announce that Professor Andrew McCallum will give a 
talk
at the AIIS seminar on Tuesday, 04/07. The talk will start from 15:30 to
16:30 in room SC 1404.  Please see the following details (including a small 
bio of Professor
Andrew McCallum).

*NOTE*: Note that the talk is on Tuesday rather than Thursday. Also note
that the room is 1404. Thanks!


Title:
Information Extraction, Data Mining and Joint Inference

Although information extraction and data mining appear together in many
applications, their interface in most current systems would better be
described as serial juxtaposition than as tight integration.
Information extraction populates slots in a database by identifying
relevant subsequences of text, but is usually not aware of the emerging
patterns and regularities in the database.  Data mining methods begin
from a populated database, and are often unaware of where the data came
from, or its inherent uncertainties.  The result is that the accuracy of
both suffers, and accurate mining of complex text sources has been
beyond reach.

In this talk I will describe work in probabilistic models that perform
joint inference across multiple components of an information processing
pipeline in order to avoid the brittle accumulation of errors.  The need
for joint inference appears not only in extraction and data mining, but
also in natural language processing, computer vision, robotics and
elsewhere.  I will argue that joint inference is one of the most
fundamental issues in artificial intelligence.
            
I will present recent work in conditional random fields for information
extraction and integration, with a focus on joint inference through
stochastic approximations, weighted first-order logic, and new methods
of probabilistic programming that enable reasoning about large-scale
data.  I'll close with a demonstration of Rexa.info, our research paper
digital library that leverages these techniques.



Joint work with colleagues at UMass: Charles Sutton, Aron Culotta,
Khashayar Rohanemanesh, Chris Pal, Greg Druck, Karl Schultz, Sameer
Singh, Pallika Kanani, Kedare Bellare, Michael Wick, Rob Hall, David
Mimno and Gideon Mann.


Bio:

Andrew McCallum is an Associate Professor and Director of the
Information Extraction and Synthesis Laboratory in the Computer Science
Department at University of Massachusetts Amherst.  He has published
over 100 papers in many areas of AI, including natural language
processing, machine learning, data mining and reinforcement learning,
and his work has received over 13,000 citations.  He received his PhD
from University of Rochester in 1995 with Dana Ballard and a
postdoctoral fellowship from CMU with Tom Mitchell and Sebastian Thrun.
Afterward he worked in an industrial research lab, where he spearheaded
the creation of CORA, an early research paper search engine that used
machine learning for spidering, extraction, classification and citation
analysis.  In the early 2000's he was Vice President of Research and
Development at at WhizBang Labs, a 170-person start-up company that used
machine learning for information extraction from the Web.  He was the
Program Co-chair for the International Conference on Machine Learning
(ICML) 2008, and a member of the boards of the International Machine
Learning Society, the CRA Community Computing Consortium and the
editorial board of the Journal of Machine Learning Research.  For the
past ten years, McCallum has been active in research on statistical
machine learning applied to text, especially information extraction,
co-reference, document classification, clustering, finite state models,
semi-supervised learning, and social network analysis.  Work on search
and bibliometric analysis of open-access research literature can be
found at http://rexa.info.  McCallum's web page is
http://www.cs.umass.edu/~mccallum.



Best,

Ming-Wei Chang