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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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| ==Information Retrieval: From Tweets to Tomes ==
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| {| border="0" cellpadding="5" cellspacing="0" align="center"
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| ! colspan="3" style="border-bottom: 3px solid grey; background: #ffefef;" | <big>Cross‐language Bayesian models for Web‐scale text analysis using MapReduce </big>
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| | style="border-right: 1px solid grey; background:#ffefef" | <b>PI</b>
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| | [http://www.umiacs.umd.edu/~jbg/ Jimmy Lin]
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| | style="border-right: 1px solid grey; background:#ffefef" | <b>Other Faculty </b>
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| | [http://www.umiacs.umd.edu/~jbg/ Jordan Boyd-Graber], [http://www.umiacs.umd.edu/~resnik/ Philip Resnik]
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| | style="border-right: 1px solid grey; background:#ffefef" | <b>Graduate Students </b>
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| | style="border-bottom: 3px solid grey; border-right: 1px solid grey; background:#ffefef" | <b>Funding</b>
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| | style="border-bottom: 3px solid grey;" | National Science Foundation 1018625
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| | style="border-bottom: 3px solid grey;" colspan="3" align="left" |
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| The goal of information retrieval is to help people find what they are looking for. Information retrieval research in the CLIP lab focuses principally on retrieval based on the language contained in text, in speech, and in document images. We work across a broad range of content types, from tweets to tomes, from talking to texting, and from Cebuano to Chinese. Three perspectives inform our work:
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| * we integrate a broad range of computational linguistics techniques,
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| * we focus on scalable techniques that can accommodate very large collections
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| * we sometimes draw the boundaries of our “systems” very broadly to include both the automated tools that we create and the process by which users can best employ those tools.
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| One example that illustrates these perspectives is our work with “cross-language information retrieval,” in which close coupling of machine translation and information retrieval techniques make it possible for people to find and use information written in languages that they can neither read nor write. Another example is our work on the design and evaluation of “question answering” systems that can automatically find and present answers to complex questions, which serves as a bridge between our work on information retrieval and summarization.
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| | style="background: #ffefef;" | Project Webpages
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| | style="background: #ffefef;" | Example Publications
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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.