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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* E-discovery
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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;" | Cross‐language Bayesian models for Web‐scale text analysis using MapReduce
 
|-
 
| PI
 
| Jimmy Lin
 
|-
 
| Other Faculty
 
| Jordan Boyd-Graber, Philip Resnik
 
|-
 
| Students
 
| Lisa Simpson
 
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| style="border-bottom: 3px solid grey;" | Funding
 
| style="border-bottom: 3px solid grey;" | NSF 1018625
 
|-
 
| colspan="3" align="center" |
 
{| border="0"
 
|+ ''A table in a table''
 
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| align="center" width="150" | [[File:Wiki.png]]
 
| align="center" width="150" | [[File:Wiki.png]]
 
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| align="center" colspan="2" style="border-top: 1px solid red;<!--
 
  --> border-right: 1px solid red; border-bottom: 2px solid red;<!--
 
  --> border-left: 1px solid red;" |
 
Two Wikipedia logos
 
|}
 
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Latest revision as of 01:51, 8 September 2017

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
  • E-discovery
  • 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.