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Research: Difference between revisions

Computational Linguistics and Information Processing
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| <b>Postdocs </b>
| <b>Postdocs </b>
|  | [http://www.umiacs.umd.edu/~hollingk/ Junhui Li]
|  | [https://languagescience.umd.edu/users/amittai Amittai Axelrod]
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| <b>Graduate Students </b>
| <b>Graduate Students </b>
| [http://www.cs.umd.edu/~vlad/ Vladimir Eidelman], Wu Ke, [http://www.cs.umd.edu/~fture/Home.html Ferhan Ture], Phil Dasler
| [https://languagescience.umd.edu/users/raosudha Sudha Rao], [http://www.cs.umd.edu/people/jmorga10 John Morgan]
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=== What we do ===
=== What we do ===


The CLIP Laboratory's current work in <b>machine translation</b> 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.
The CLIP Laboratory's current work in <b>machine translation</b> 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 linguistically informed translation model features, the use of unsupervised methods in translation modeling, exploitation of large scale "cloud computing" methods, and real-time incremental translation.


=== Some Recent Publications ===
=== Some Recent Publications ===

Revision as of 23:25, 18 February 2015


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, Thang Nguyen

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

Machine Translation

Faculty Philip Resnik, Hal Daumé III, Bonnie Dorr
Postdocs Amittai Axelrod
Graduate Students Sudha Rao, John Morgan

What we do

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 linguistically informed translation model features, the use of unsupervised methods in translation modeling, exploitation of large scale "cloud computing" methods, and real-time incremental translation.

Some Recent Publications

Some Project Pages

Paraphrase

Faculty Bonnie Dorr, Philip Resnik
Students Yakov Kronrod

What we do

Paraphrase, 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.

Publications

Text Summarization

Faculty Bonnie Dorr David Zajic Hal Daumé III

What we do

Text Summarization is the creation of a short document to serve as a surrogate for a longer document. The CLIP Laboratory's approach to summarization enhances the extractive method of selecting source document sentences for inclusion in a summary by using sentence compression to enlarge the pool of available sentences, and by combining fluent text with topic terms. Our sentence compression technology has encompassed both statistical and linguistic methodologies. We have developed an extrinsic evaluation measure for summarization, Relevance Prediction, which is grounded in a real-world task using summarized documents. The CLIP Laboratory, in collaboration with BBN, has been a regular participant in NIST's summarization evaluations (Document Understanding Conferences and Text Analysis Conferences), and has contributed summarization components to DARPA Translingual Information Detection, Extraction and Summarization (TIDES), Surprise Language Exercise (SLE), and Global Autonomous Language Exploitation (GALE) programs, and to the iOpener project.


Parsing and Tagging

Faculty Mary Harper
Graduate Students Vladimir Eidelman

Publications

Computational Social Science

Faculty Bonnie Dorr, Jimmy Lin, Douglas W. Oard, Louiqa Raschid, Philip Resnik, Amy Weinberg
Graduate Students Viet-An Nguyen, Hassan Sayyadi, Shanchan Wu, Thang Nguyen, Weiwei Yang

What we do

Computational social science involves the use of computational methods and models to leverage "the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors". Research in the CLIP Laboratory is at the forefront of this emerging area, and includes sentiment analysis (computational modeling and prediction of opinions, perspective, and other private states), automatic analysis and visualization of the scientific literature, modeling the diffusion of technological innovations, modeling and prediction of social goals and actions such as persuasion, monitoring and prediction (tracking events, predicting new links or articles), recommendation (personalized recommendations, learning to rank), computational modeling of political discourse, and looking at linguistic signals related to mental health.

Examples of Publications

Information Retrieval

Faculty Jimmy Lin, Douglas W. Oard
Postdocs Jiaul Paik
Graduate Students Mossaab Bagdouri, Ning Gao,

Rashmi Sankepally, Jyothi Vinjumur, Tan Xu, Thang Nguyen

What we do

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:

  • we integrate a broad range of computational linguistics techniques,
  • we focus on scalable techniques that can accommodate very large collections
  • 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.

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.

Publications

Disambiguation

Faculty Jordan Boyd-Graber, Judith Klavans, Philip Resnik
Graduate Students Raul David Guerra

What we do

Disambiguation is the process of determining the meaning or senses of a word in its context; disambiguation remains one of the most challenging NLP problems since discovering word senses involves syntactic, semantic and pragmatic contextual inferencing, along with a rich knowledge base to base selection upon. For example, the word "wing" in the theater differs from airplanes, yet another sense for furniture ("wing chair") applies to some usages. Often disambiguation can be based on windows of two and three words, but usually involves larger computation. Techniques for disambiguation range from the use of large scale thesaural resources (such as WordNet) to purely statistical methods.

Publications

Annotation and Sense-making

Faculty Judith Klavans Louiqa Raschid
Graduate Students Hassan Sayyadi Shanchan Wu

What we do

Annotation and tagging are ways to enhance knowledge in structured or semi-structured resources. Annotation typically references terms from a controlled vocabulary or ontology and is popular in bibliographic, scientific or museum collections. Tagging is more common in social media to tag images and documents and of course the now ubiquitous hashtags tweets. Sense-making or discovery is the process of extracting knowledge from these annotated or tagged resources and could range from simple counting to data/text mining to graph pattern recognition.

In the CLIP lab, we are interested in the following tasks:

  • Tagging and sense-making
  • Pattern discovery in annotated graph datasets from the biomedical domain.
  • Data mining with Linked Data.

Publications

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