Natural language research announcements

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

Dear faculty and students,

A Ph.D. candidate of CS department, Kevin Small, will give a talk (details
below) for the AIIS seminar at 4:00 pm, Mar 19th (this Thursday). The
room number is 3405. Hope to see you there!

Interactive Learning Protocols for Natural Language Applications

Statistical machine learning has become an integral technology for
solving many informatics applications. In particular, corpus-based
statistical techniques have emerged as the dominant paradigm for core
natural language processing (NLP) tasks including parsing, machine
translation, and information extraction. However, while supervised
machine learning is well understood, its successful application to
practical scenarios incur significant costs associated with annotating
large data sets and feature engineering.

In this talk, I will describe methods for reducing annotation costs
and improving system performance through interactive learning
protocols. The first part of the talk describes my research on active
learning strategies for the structured output and pipeline model
settings, two widely-used models for complex application scenarios
where obtaining labeled data is particularly expensive. Secondly, I
will introduce the interactive feature space construction protocol,
which uses a more sophisticated interaction to incrementally add
application-targeted domain knowledge into the feature space to improve
performance and reduce the need for labeled data. I will also present
empirical results for the semantic role labeling and named
entity/relation extraction NLP tasks, demonstrating state of the art
performance with significantly reduced annotation requirements.

BIO:

Kevin Small is a Ph.D. candidate in the Department of Computer Science
at the University of Illinois at Urbana-Champaign. His research
interests are in the areas of machine learning, natural language
processing, and artificial intelligence. At UIUC, he is a member of
the Cognitive Computation Group under the direction of Professor Dan
Roth. Kevin’s primary research results concern using interactive
learning protocols to improve the performance of machine learning
algorithms while reducing sample complexity.


Best,

Ming-Wei