Research

Bayesian Modeling

Faculty	Jordan Boyd-Graber Jimmy Lin Hal Daumé III automated models; multilingual models; nonparametric methods 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

Faculty	Bonnie Dorr interlingual and hybrid machine translation, MT evaluation Mary Harper multilingual parsing, language modeling Philip Resnik linguistically informed translation modeling, crowdsourcing and translation Hal Daumé III domain adaptation for translation; translation with linguistic universals
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. Representative Publications and Project Pages: Greene and Resnik, NAACL 2009: More Than Words: Syntactic Packaging and Implicit Sentiment

Paraphrasing

Faculty	Bonnie Dorr paraphrasing, summarization, language understanding Philip Resnik linguistically informed NLP, paraphrasing
Postdocs
Graduate Students
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.

Text Summarization

Faculty	Bonnie Dorr evaluation David Zajic sentence compression, sentence selection Hal Daumé III summarization of technical documents; sentence compression
Postdocs
Graduate Students
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 latent variable parsing, speech
Postdocs
Graduate Students	Vladimir Eidelman Zhongqiang Huang
Parsing involves Representative Publications and Project Pages: Huang and Harper, EMNLP 2009: Self-Training PCFG Grammars with Latent Annotations Across Languages Huang, Eidelman and Harper NAACL 2009: Improving A Simple Bigram HMM Part-of-Speech Tagger by Latent Annotation and Self-Training

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, recommendation 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, 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). Representative Publications and Project Pages: Greene and Resnik, NAACL 2009: More Than Words: Syntactic Packaging and Implicit Sentiment Boyd-Graber and Resnik, EMNLP 2010: Holistic Sentiment Analysis Across Languages: Multilingual Supervised Latent Dirichlet Allocation

Information Retrieval

Faculty	Jimmy Lin Douglas W. Oard
Postdocs	Earl Wagner
Graduate Students	Lidan Wang, Tan Xu
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.

Disambiguation

Faculty	Jordan Boyd-Graber Judith Klavans Philip Resnik
Postdocs
Graduate Students	Raul David Guerra
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 Philip Resnik and David Yarowsky, "Distinguishing Systems and Distinguishing Senses: New Evaluation Methods for Word Sense Disambiguation", Natural Language Engineering 5(2), pp. 113-133.

Annotation and Sense-making

Faculty	Judith Klavans Louiqa Raschid
Postdocs
Graduate Students	Hassan Sayyadi Shanchan Wu
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. Representative Publications and Project Pages: Pattern Discovery, Validation, and Hypothesis Development from the Annotated Biological Web