Research: Difference between revisions
Computational Linguistics and Information Processing
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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> | ! 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> | | style="border-right: 1px solid grey; background:#ffefef" | <b>Faculty</b> | ||
| [http://www.umiacs.umd.edu/~ | | [http://www.umiacs.umd.edu/~jimmylin/ Jimmy Lin] [http://www.umiacs.umd.edu/~oard/ Doug Oard] | ||
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| style="border-right: 1px solid grey; background:#ffefef" | <b> | | style="border-right: 1px solid grey; background:#ffefef" | <b>Postdocs </b> | ||
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| style="border-right: 1px solid grey; background:#ffefef" | <b>Graduate Students </b> | | style="border-right: 1px solid grey; background:#ffefef" | <b>Graduate Students </b> | ||
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| style="border-bottom: 3px solid grey;" colspan="3" align="left" | | | style="border-bottom: 3px solid grey;" colspan="3" align="left" | | ||
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: | 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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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. | 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. | ||
<b>Representative Publications:</b> | |||
* Publication 1 | |||
* Publication 2 | |||
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Revision as of 01:35, 13 August 2010
Machine Translation
Summarization
Parsing and Tagging
Sentiment Analysis
Information Retrieval: From Tweets to Tomes
| Cross‐language Bayesian models for Web‐scale text analysis using MapReduce | ||
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| Faculty | Jimmy Lin Doug Oard | |
| Postdocs | ||
| Graduate Students | ||
| Funding | National Science Foundation 1018625 | |
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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:
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. Representative Publications:
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