Actions

Research: Difference between revisions

Computational Linguistics and Information Processing
No edit summary
 
(72 intermediate revisions by 8 users not shown)
Line 1: Line 1:
==Bayesian Modeling==
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:


{| border="0" cellpadding="5" cellspacing="0" align="center"
* Computational psycholinguistics
|-
* Computational social science
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Faculty</b>
* Cross-language information retrieval
|
* Data science for finance / social good
{| border="0"
* Deep learning
| [http://www.umiacs.umd.edu/~jbg Jordan Boyd-Graber] ||
* Pattern discover in graphs / ranking and recommendation
|-
* Human-in-the-loop machine learning
| [http://www.umiacs.umd.edu/~jimmylin Jimmy Lin] ||
* Machine translation
|-
* Mental health
| [http://umiacs.umd.edu/~hal Hal Daum&eacute; III] || automated models; multilingual models; nonparametric methods
* Privacy-aware information retrieval
|-
* Speech retrieval
| [http://www.umiacs.umd.edu/~resnik/ Philip Resnik] ||
* Urban computing / smart environments
|-
|}
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top"  | <b>Postdocs </b>
|
|-
| style="border-bottom: 3px solid grey; border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Graduate Students </b>
| style="border-bottom: 3px solid grey;" | Eric Hardisty, [http://umiacs.umd.edu/~ynhu/ Yuening Hu], Ke Zhai
|-
| colspan="3" align="left" |


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.
CLIP research has been supported by the following organizations: NSF, DARPA, ARL, IARPA, OFR (Treasury), NIST, IMLS, Google, Yahoo and the World Bank.
 
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 to the web scale
* understanding how humans interpret and understand the latent variables in Bayesian models
 
<b>Representative Publications and Project Pages:</b>
* [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://drum.lib.umd.edu/handle/1903/10058 Gibbs Sampling for the Uninitiated]
|}
 
==Machine Translation==
 
{| border="0" cellpadding="5" cellspacing="0" align="center"
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Faculty</b>
|
{| border="0"
| [http://www.umiacs.umd.edu/~bonnie Bonnie Dorr] || interlingual and hybrid machine translation, MT evaluation
|-
| [http://umiacs.umd.edu/~mharper Mary Harper] || multilingual parsing, language modeling
|-
| [http://www.umiacs.umd.edu/~resnik/ Philip Resnik] || linguistically informed translation modeling, crowdsourcing and translation
|-
| [http://www.umiacs.umd.edu/~hal/ Hal Daum&eacute; III] || domain adaptation for translation; translation with linguistic universals
|-
|}
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Postdocs </b>
|
|-
| style="border-bottom: 3px solid grey; border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Graduate Students </b>
| style="border-bottom: 3px solid grey;" | [http://www.cs.umd.edu/~vlad/ Vlad Eidelman]
|-
| colspan="3" align="left" |
 
The CLIP Laboratory's work in <b>machine translation</b> continues the lab's long tradition of research in translation.  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.
 
<b>Representative Publications and Project Pages:</b>
* Greene and Resnik, NAACL 2009: [http://umiacs.umd.edu/~resnik/pubs/greene_resnik_naacl2009.pdf More Than Words: Syntactic Packaging and Implicit Sentiment]
|}
 
==Paraphrasing==
 
{| border="0" cellpadding="5" cellspacing="0" align="center"
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Faculty</b>
|
{| border="0"
| [http://www.umiacs.umd.edu/~bonnie Bonnie Dorr] || paraphrasing, summarization, language understanding
|-
| [http://www.umiacs.umd.edu/~resnik/ Philip Resnik] || linguistically informed NLP, paraphrasing
 
|-
|}
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Postdocs </b>
|
|-
| style="border-bottom: 3px solid grey; border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Graduate Students </b>
| style="border-bottom: 3px solid grey;" |
|-
| colspan="3" align="left" |
 
<b>Paraphrase</b>, 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. 
 
|}
 
==Text Summarization ==
 
{| border="0" cellpadding="5" cellspacing="0" align="center"
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Faculty</b>
|
{| border="0"
| [http://www.umiacs.umd.edu/~bonnie Bonnie Dorr] || evaluation
|-
| David Zajic || sentence compression, sentence selection
|-
| [http://www.umiacs.umd.edu/~hal Hal Daum&eacute; III] || summarization of technical documents; sentence compression
|-
|}
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Postdocs </b>
|
|-
| style="border-bottom: 3px solid grey; border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Graduate Students </b>
| style="border-bottom: 3px solid grey;" |
|-
| colspan="3" align="left" |
 
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==
 
==Computational Social Science==
 
 
{| border="0" cellpadding="5" cellspacing="0" align="center"
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Faculty</b>
|
{| border="0"
| [http://www.umiacs.umd.edu/~jbg/ Jordan Boyd-Graber] || scientific literature analysis, persuasion
|-
| [http://www.umiacs.umd.edu/~bonnie Bonnie Dorr] || sentiment analysis, scientific literature analysis
|-
| [http://www.umiacs.umd.edu/~jimmylin/ Jimmy Lin] || social media
|-
|  [http://www.umiacs.umd.edu/~oard/ Douglas W. Oard] || topical relation detection
|-
| [http://www.umiacs.umd.edu/~louiqa/ Louiqa Raschid] || diffusion, prediction, event detection, recommendation
|-
| [http://www.umiacs.umd.edu/~resnik/ Philip Resnik] || sentiment, persuasion
|-
| [http://www.umiacs.umd.edu/~weinberg Amy Weinberg] || sentiment, persuasion
|}
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Postdocs </b>
|
|-
| style="border-bottom: 3px solid grey; border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Graduate Students </b>
| style="border-bottom: 3px solid grey;" | [http://www.cs.umd.edu/~hardisty/ Eric Hardisty] [http://www.umiacs.umd.edu/~asayeed/ Asad Sayed] [http://www.cs.umd.edu/~sayyadi Hassan Sayyadi] [http://www.cs.umd.edu/~wsc Shanchan Wu]
|-
| colspan="3" align="left" |
 
<b>Computational social science</b> involves the use of computational methods and models to leverage [http://www.sciencemag.org/cgi/content/summary/323/5915/721 "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) and recommendation (personalized recommendations, learning to rank).
 
<b>Representative Publications and Project Pages:</b>
* Greene and Resnik, NAACL 2009: [http://umiacs.umd.edu/~resnik/pubs/greene_resnik_naacl2009.pdf More Than Words: Syntactic Packaging and Implicit Sentiment]
* Boyd-Graber and Resnik, EMNLP 2010: [http://www.umiacs.umd.edu/~jbg/docs/jbg-mlslda-2010.pdf Holistic Sentiment Analysis Across Languages: Multilingual Supervised Latent Dirichlet Allocation]
 
|}
 
==Information Retrieval ==
 
{| border="0" cellpadding="5" cellspacing="0" align="center"
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Faculty</b>
|
{| border="0"
| [http://www.umiacs.umd.edu/~jimmylin/ Jimmy Lin] ||
|-
| [http://terpconnect.umd.edu/~oard/ Douglas W. Oard] ||
|-
|}
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Postdocs </b>
|
{| border="0"
| [http://www.umiacs.umd.edu/~ewagner/ Earl Wagner] ||
|}
|-
| style="border-bottom: 3px solid grey; border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Graduate Students </b>
| style="border-bottom: 3px solid grey;" | Lidan Wang, [http://terpconnect.umd.edu/~tanx/ Tan Xu]
|-
| 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:
* 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.
 
<b>Representative Publications and Project Pages:</b>
* Douglas W. Oard, "[http://terpconnect.umd.edu/~oard/pdf/elis09.pdf Multilingual Information Access]," in Encyclopedia of Library and Information Sciences, 3rd Ed., 2009.
 
 
|}
 
==Disambiguation==
 
{| border="0" cellpadding="5" cellspacing="0" align="center"
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Faculty</b>
|
{| border="0"
| [http://www.umiacs.umd.edu/~jbg Jordan Boyd-Graber] ||
|-
| [http://www.umiacs.umd.edu/~jklavans Judith Klavans] ||
|-
| [http://www.umiacs.umd.edu/~resnik/ Philip Resnik] ||
|-
|}
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Postdocs </b>
|
|-
| style="border-bottom: 3px solid grey; border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Graduate Students </b>
| style="border-bottom: 3px solid grey;" | Raul David Guerra
|-
|  colspan="3" align="left" |
 
<b>Disambiguation </b> 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.
 
<b>Representative Publications and Project Pages:</b>
* [http://www.umiacs.umd.edu/research/t3/index.shtml T3: Text, Tags, Trust]
* [http://www.umiacs.umd.edu/~jbg/docs/jbg-EMNLP07.pdf A Topic Model for Word Sense Disambiguation]
* Philip Resnik and David Yarowsky, "[http://www.cs.jhu.edu/~yarowsky/pubs/nle00.ps Distinguishing Systems and Distinguishing Senses: New Evaluation Methods for Word Sense Disambiguation]", Natural Language Engineering 5(2), pp. 113-133.
 
|}
 
==Annotation and Sense-making==
 
{| border="0" cellpadding="5" cellspacing="0" align="center"
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Faculty</b>
|
{| border="0"
 
| [http://www.umiacs.umd.edu/~jklavan Judith Klavans] ||
|-
| [http://www.umiacs.umd.edu/~louiqa Louiqa Raschid] ||
|-
 
 
|-
|}
|-
| style="border-right: 1px solid grey; background:#ffefef" valign="top"  | <b>Postdocs </b>
|
|-
| style="border-bottom: 3px solid grey; border-right: 1px solid grey; background:#ffefef" valign="top" | <b>Graduate Students </b>
| style="border-bottom: 3px solid grey;" | [http://www.cs.umd.edu/~sayyadi Hassan Sayyadi] [http://www.cs.umd.edu/~wsc Shanchan Wu]
|-
| colspan="3" align="left" |
 
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.
 
<b>Representative Publications and Project Pages:</b>
* [http://www.umiacs.umd.edu/labs/CLIP/RSEAGER2009/index2.html Pattern Discovery, Validation, and Hypothesis Development from the Annotated Biological Web ]
 
|}

Latest revision as of 15:41, 23 September 2020

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.

Retrieved from "https://wiki.umiacs.umd.edu/clip/index.php?title=Research&oldid=1026"