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− | + | =Bayesian Modeling= | |
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− | | | + | | | <b>Faculty</b> |
− | | | + | | [http://www.umiacs.umd.edu/~jbg Jordan Boyd-Graber], [http://terpconnect.umd.edu/~naomi Naomi Feldman], [http://umiacs.umd.edu/~hal Hal Daumé III], [http://www.umiacs.umd.edu/~resnik/ Philip Resnik] |
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− | | | + | | | <b>Postdocs </b> |
− | + | | [http://www.umiacs.umd.edu/~tsmoon/ Taesun Moon] | |
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− | | [http://www.umiacs.umd.edu/~ | ||
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+ | | <b>Graduate Students </b> | ||
+ | | [http://www.cs.umd.edu/~vietan/ Viet-An Nguyen] [http://umiacs.umd.edu/~ynhu/ Yuening Hu], Ke Zhai | ||
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− | + | === What We Do === | |
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Bayesian modeling is a rigorous mathematical formalism that allows us to build systems that reflect our uncertainty about the world. Applied to language, they allow us to build models that reflect the "latent" aspects of communication such as topic, part of speech, syntax, or sentiment. Using posterior inference, we can use the models to discover the latent features that best explain observed language. | Bayesian modeling is a rigorous mathematical formalism that allows us to build systems that reflect our uncertainty about the world. Applied to language, they allow us to build models that reflect the "latent" aspects of communication such as topic, part of speech, syntax, or sentiment. Using posterior inference, we can use the models to discover the latent features that best explain observed language. | ||
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* understanding how humans interpret and understand the latent variables in Bayesian models | * understanding how humans interpret and understand the latent variables in Bayesian models | ||
− | + | === Publications === | |
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* [http://www.umiacs.umd.edu/~hal/HBC/ Hierarchical Bayes Compiler] | * [http://www.umiacs.umd.edu/~hal/HBC/ Hierarchical Bayes Compiler] | ||
* [http://www.umiacs.umd.edu/~jbg/docs/nips2009-rtl.pdf Reading Tea Leaves: How Humans Interpret Topic Models] | * [http://www.umiacs.umd.edu/~jbg/docs/nips2009-rtl.pdf Reading Tea Leaves: How Humans Interpret Topic Models] | ||
* [http://drum.lib.umd.edu/handle/1903/10058 Gibbs Sampling for the Uninitiated] | * [http://drum.lib.umd.edu/handle/1903/10058 Gibbs Sampling for the Uninitiated] | ||
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==Machine Translation== | ==Machine Translation== |
Revision as of 13:53, 11 December 2012
Contents
Bayesian Modeling
Faculty | Jordan Boyd-Graber, Naomi Feldman, Hal Daumé III, Philip Resnik |
Postdocs | Taesun Moon |
Graduate Students | Viet-An Nguyen Yuening Hu, Ke Zhai |
What We Do
Bayesian modeling is a rigorous mathematical formalism that allows us to build systems that reflect our uncertainty about the world. Applied to language, they allow us to build models that reflect the "latent" aspects of communication such as topic, part of speech, syntax, or sentiment. Using posterior inference, we can use the models to discover the latent features that best explain observed language.
In the CLIP lab, we are interested in
- building tools that make it easier for people to work with Bayesian models
- scaling inference for Bayesian models up to Web scale
- understanding how humans interpret and understand the latent variables in Bayesian models
Publications
- Hierarchical Bayes Compiler
- Reading Tea Leaves: How Humans Interpret Topic Models
- Gibbs Sampling for the Uninitiated
Machine Translation
Faculty |
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Postdocs | Kristy Hollingshead | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Graduate Students | Vladimir Eidelman | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The CLIP Laboratory's current work in machine translation continues the lab's long tradition of research in this area. Like most of the field, we work within the framework of statistical MT, but with an emphasis on taking appropriate advantage of knowledge driven or linguistically informed model structures, features, and priors. Some current areas of research include syntactically informed language models, linguistically informed translation model features, the use of unsupervised methods in translation modeling, exploitation of large scale "cloud computing" methods, and human-machine collaborative translation via crowdsourcing. Some Representative Publications:
Some Project Pages ParaphraseParaphrase, the ability to express the same meaning in multiple ways, is an active area of research within the NLP community and here in the CLIP Laboratory. Our work in paraphrase includes the use of paraphrase in MT evaluation and parameter estimation, lattice and forest translation, and collaborative translation, as well as research on lexical and phrasal semantic similarity measures, meaning preservation in machine translation and summarization, and large-scale document similarity computation via cloud computing methods.
Some Project Pages Some Representative Publications
Text Summarization
Parsing and Tagging
Computational Social Science
Information Retrieval
Disambiguation
Annotation and Sense-making
Recent Accomplishments in the last 12 monthsJordan Boyd-Graber Hal Daume III David Doermann Bonnie Dorr Jimmy Lin Doug Oard Louiqa Raschid PattArAn: NSF grant and collaboration with plant biologists. SM3: NSF grant and multiple papers in collaboration with Shanchan Wu and Hassan Sayyadi and Bill Rand. PAnG: Tool for graph data mining of annotated graph datasets; collaboration with Samir Khuller and multiple papers. Next Generation Financial Cyberinfrastructure: Workshops in July 2010 and July 2012 sponsored by the NSF and CRA/CCC. Philip Resnik |