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[nl-uiuc] Upcoming talk at the AIIS seminar


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  • From: Ming-Wei Chang <mchang21 AT uiuc.edu>
  • To: nl-uiuc AT cs.uiuc.edu, aivr AT cs.uiuc.edu, dais AT cs.uiuc.edu, cogcomp AT cs.uiuc.edu, vision AT cs.uiuc.edu, krr-group AT cs.uiuc.edu, aiis AT cs.uiuc.edu
  • Subject: [nl-uiuc] Upcoming talk at the AIIS seminar
  • Date: Thu, 04 Jun 2009 12:18:49 -0500
  • List-archive: <http://lists.cs.uiuc.edu/pipermail/nl-uiuc>
  • List-id: Natural language research announcements <nl-uiuc.cs.uiuc.edu>


Dear faculty and students,

Next week we will have Dr. Einat Minkov in the AIIS
seminar (details below) at 11:00 am, June 9-th (next Tuesday
morning). The room number is SC 3405. Hope to see you there!


Title:
Learning to Query Heterogeneous Data

Abstract:
Structured data, describing entities and their inter-relations, can be
accommodated and processed using relational databases. However, there
is much information available from unstructured or semi-structured
sources that we would like to query and reason about. In this talk, I
will describe a query language that is applied to a graph containing a
heterogeneous mixture of textual and non-textual objects. Random graph
walk paradigms (e.g., Personalized PageRank) are used to rank the
entities in the graph by their similarity, or relatedness, to a query.
I will show that multiple tasks in a given domain can be casted as
search queries in this framework. While graph walks provide good
performance, machine learning techniques can be applied to adapt the
generated similarity metric per task. In the talk, I will include an
experimental evaluation of several classes of similarity queries from
the domain of personal information management, where email messages,
meeting entries and social network information extracted from a
personal workstation are represented as a graph; for instance, we use
similarity search to find people likely to attend a meeting. A second
domain evaluated is the processing of parsed text as an
entity-relation graph, where we use the graph-based similarity measure
to extract city and person names from textual corpora.

Bio:
Einat Minkov received her bachelor's and Master's degrees in
Industrial Engineering from Tel Aviv University in 1999 and 2000, and
a PhD degree in Language and Information Technologies from Carnegie
Mellon University in 2008. Currently, Dr. Minkov works at Nokia
Research labs in Cambridge, MA. Previously, she worked at 3Com and at
Amdocs Israel, and visited Microsfot Research labs in Redmond, WA. Dr.
Minkov research interests include information extraction and
integration, natural language processing and machine learning. Her
publications may be found at her home page: http://cs.cmu.edu/~einat.



Best,

Ming-Wei




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