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| ==Machine Translation==
| | 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: |
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| ==Summarization ==
| | * 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 |
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| ==Parsing and Tagging==
| | CLIP research has been supported by the following organizations: NSF, DARPA, ARL, IARPA, OFR (Treasury), NIST, IMLS, Google, Yahoo and the World Bank. |
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| ==Sentiment Analysis==
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| ==Bayesian Modeling==
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| {| border="0" cellpadding="5" cellspacing="0" align="center"
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| ! colspan="3" style="background: #ffefef;" | Cross‐language Bayesian models for Web‐scale text analysis using MapReduce
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| | PI
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| | Jimmy Lin
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| | Other Faculty
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| | Jordan Boyd-Graber, Philip Resnik
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| | Graduate Students
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| | style="border-bottom: 3px solid grey;" | Funding
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| | style="border-bottom: 3px solid grey;" | NSF 1018625
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| | style="border-bottom: 3px solid grey;" colspan="3" align="left" |
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| The Web promises unprecedented access to the perspectives of an
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| enormous number of people on a wide range of issues. Turning that
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| still untamed cacophony into meaningful insights requires dealing with
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| the linguistic diversity and scale of the Web. Most current research
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| focuses on specialized tasks such as tracking consumer opinions, and
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| virtually all current research treats the Web as both monolithic and
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| monolingual, ignoring the variety of languages represented and the
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| rich interplay between topics and issues under discussion.
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| This project moves the state of the art forward by focusing on two key
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| challenges. First, highly-scalable MapReduce algorithms for
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| linguistic modeling within a Bayesian framework, making use of
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| variational inference to achieve a high degree of parallelization on
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| Web-scale datasets. Second, novel Bayesian models that learn
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| consistent interpretations of text across languages and a wide range
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| of response variables of interest (for example, views on an issue,
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| strength of emotion relative to an event, and focus of attention).
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| The techniques developed in this project will be demonstrated on large
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| crawls of Web pages and blogs. Potential applications for these
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| technologies include helping a schoolchild learn that people in
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| different countries may view some issues very differently, helping a
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| politician understand how constituents are reacting to proposed
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| legislation, or helping an intelligence analyst understand how public
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| opinion is evolving in a hostile country.
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| | Project Webpage
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| | Publications
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| |}
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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:
- 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.