Difference between revisions of "Research"

(Bayesian Modeling)
 
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==Machine Translation==
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In the CLIP lab, we approach research on computational linguistics and information processing from a variety of angles. Some of our ongoing projects focus on the following challenges:
  
==Summarization ==
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* Computational psycholinguistics
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* Computational social science
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* Cross-language information retrieval
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* Data science for finance / social good
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* Deep learning
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* Pattern discover in graphs / ranking and recommendation
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* Human-in-the-loop machine learning
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* Machine translation
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* Mental health
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* Privacy-aware information retrieval
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* Speech retrieval
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* Urban computing / smart environments
  
==Parsing and Tagging==
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CLIP research has been supported by the following organizations: NSF, DARPA, ARL, IARPA, OFR (Treasury), NIST, IMLS, Google, Yahoo and the World Bank.
 
 
==Sentiment Analysis==
 
 
 
==Bayesian Modeling==
 
 
 
{| border="0" cellpadding="5" cellspacing="0" align="center"
 
|-
 
! colspan="3" style="background: #ffefef;" | <big>Cross‐language Bayesian models for Web‐scale text analysis using MapReduce </big>
 
|-
 
| style="border-right: 1px solid grey; background:#ffefef" | <b>PI</b>
 
| Jimmy Lin
 
|-
 
| style="border-right: 1px solid grey; background:#ffefef" | <b>Other Faculty </b>
 
| [http://www.umiacs.umd.edu/~jbg/ Jordan Boyd-Graber], [http://www.umiacs.umd.edu/~resnik/ Philip Resnik]
 
|-
 
| style="border-right: 1px solid grey; background:#ffefef" | <b>Graduate Students </b>
 
|
 
|-
 
| style="border-bottom: 3px solid grey; border-right: 1px solid grey; background:#ffefef" | <b>Funding</b>
 
| style="border-bottom: 3px solid grey;" | NSF 1018625
 
|-
 
| style="border-bottom: 3px solid grey;" colspan="3" align="left" |
 
 
 
The Web promises unprecedented access to the perspectives of an
 
enormous number of people on a wide range of issues.  Turning that
 
still untamed cacophony into meaningful insights requires dealing with
 
the linguistic diversity and scale of the Web.  Most current research
 
focuses on specialized tasks such as tracking consumer opinions, and
 
virtually all current research treats the Web as both monolithic and
 
monolingual, ignoring the variety of languages represented and the
 
rich interplay between topics and issues under discussion.
 
 
 
This project moves the state of the art forward by focusing on two key
 
challenges.  First, highly-scalable MapReduce algorithms for
 
linguistic modeling within a Bayesian framework, making use of
 
variational inference to achieve a high degree of parallelization on
 
Web-scale datasets.  Second, novel Bayesian models that learn
 
consistent interpretations of text across languages and a wide range
 
of response variables of interest (for example, views on an issue,
 
strength of emotion relative to an event, and focus of attention).
 
 
 
The techniques developed in this project will be demonstrated on large
 
crawls of Web pages and blogs.  Potential applications for these
 
technologies include helping a schoolchild learn that people in
 
different countries may view some issues very differently, helping a
 
politician understand how constituents are reacting to proposed
 
legislation, or helping an intelligence analyst understand how public
 
opinion is evolving in a hostile country.
 
 
 
|-
 
| Project Webpage
 
| Publications
 
|-
 
 
 
|}
 

Latest revision as of 15:41, 23 September 2020

In the CLIP lab, we approach research on computational linguistics and information processing from a variety of angles. Some of our ongoing projects focus on the following challenges:

  • Computational psycholinguistics
  • Computational social science
  • Cross-language information retrieval
  • Data science for finance / social good
  • Deep learning
  • Pattern discover in graphs / ranking and recommendation
  • Human-in-the-loop machine learning
  • Machine translation
  • Mental health
  • Privacy-aware information retrieval
  • Speech retrieval
  • Urban computing / smart environments

CLIP research has been supported by the following organizations: NSF, DARPA, ARL, IARPA, OFR (Treasury), NIST, IMLS, Google, Yahoo and the World Bank.