Research

Bayesian Modeling

Faculty	Jordan Boyd-Graber Jimmy Lin Hal Daume III Philip Resnik
Postdocs
Graduate Students	Eric Hardisty, Yuening Hu, Ke Zhai
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 to the web scale understanding how humans interpret and understand the latent variables in Bayesian models Representative Publications and Project Pages: Hierarchical Bayes Compiler Reading Tea Leaves: How Humans Interpret Topic Models Gibbs Sampling for the Uninitiated

Machine Translation and Paraphrasing

Faculty	Bonnie Dorr interlingual and hybrid MT, semantically-informed syntactic MT Mary Harper multilingual parsing, language modeling Philip Resnik linguistically informed translation modeling, paraphrase, crowdsourcing and translation
Postdocs
Graduate Students	Vlad Eidelman
The CLIP Laboratory's work in machine translation 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. 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. Representative Publications and Project Pages: Greene and Resnik, NAACL 2009: More Than Words: Syntactic Packaging and Implicit Sentiment

Computational Social Science

Faculty	Jordan Boyd-Graber scientific literature analysis, persuasion Bonnie Dorr sentiment analysis, scientific literature analysis Jimmy Lin social media Douglas W. Oard topical relation detection Louiqa Raschid diffusion, prediction, event detection Philip Resnik sentiment, persuasion Amy Weinberg sentiment, persuasion
Postdocs
Graduate Students	Eric Hardisty Asad Sayed Hassan Sayyadi Shanchan Wu
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, and modeling and prediction of social goals and actions such as persuasion. Representative Publications and Project Pages: Greene and Resnik, NAACL 2009: More Than Words: Syntactic Packaging and Implicit Sentiment

Information Retrieval

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. Representative Publications and Project Pages:

• Douglas W. Oard, "| Multilingual Information Access," in Encyclopedia of Library and Information Sciences, 3rd Ed., 2009.

Faculty	Jimmy Lin Doug Oard
Postdocs	Earl Wagner
Graduate Students		Tan Xu

Disambiguation

Faculty	Jordan Boyd-Graber Judith Klavans Philip Resnik
Postdocs
Graduate Students
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. Representative Publications and Project Pages: T3: Text, Tags, Trust A Topic Model for Word Sense Disambiguation