Events

Colloquia

Titles and abstracts appear after the calendar. Talks are held at 11AM in AV Williams 3258 unless otherwise noted. All are welcome. Typically, external speakers have slots for one-on-one meetings with Maryland researchers. Contact the host if you'd like to have a meeting.

Google Calendar for CLIP Speakers

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Spring 2011 Speakers

February 23, Sinead Williamson: Nonparametric Bayesian models for dependent data

A priori assumptions about the number of parameters required to model our data are often unrealistic. Bayesian nonparametric models circumvent this problem by assigning prior mass to a countably infinite set of parameters, only a finite (but random) number of which will contribute to a given data set. Over recent years, a number of authors have presented dependent nonparametric models -- distributions over collections of random measures associated with values in some covariate space. While the properties of these random measures are allowed to vary across the covariate space, the marginal distribution at each covariate value is given by a known nonparametric distribution. Such distributions are useful for modelling data that vary with some covariate: in image segmentation, proximal pixels are likely to be assigned to the same segment; in modelling documents, topics are likely to increase and decrease in popularity over time.

Most dependent nonparametric models in the literature have Dirichlet process-distributed marginals. While the Dirichlet process is undeniably the most commonly used discrete nonparametric Bayesian prior, this ignores a wide range of interesting models. In my PhD, I have focused on dependent nonparametric models beyond the Dirichlet process -- in particular, on dependent nonparametric models based on the Indian buffet process, a distribution over binary matrices with an infinite number of columns. In this talk, I will give a general introduction to dependent nonparametric models, and describe some of the work I have done in this area.

Bio: Sinead Williamson is a PhD student working with Zoubin Ghahramani at the University of Cambridge, UK. Her main research interests are dependent nonparametric processes and nonparametric latent variable models. She will be visiting the University of Maryland for six months before starting a post doc at Carnegie Mellon University in the Fall.

February 16, Ophir Frieder: Humane Computing

Humane Computing is the design, development, and implementation of computing systems that directly focus on improving the human condition or experience. In that light, three efforts are presented, namely, improving foreign name search technology, spam detection algorithms for peer-to-peer file sharing systems, and novel techniques for urinary tract infection treatment.

The first effort is in support of the Yizkor Books project of the Archives Section of the United States Holocaust Memorial Museum. Yizkor Books are commemorative, firsthand accounts of communities that perished before, during, and after the Holocaust. Users of such volumes include historians, archivists, educators, and survivors. Since Yizkor collections are written in 13 different languages, searching them is difficult. In this effort, novel foreign name search approaches which favorably compare against the state of the art are developed. By segmenting names, fusing individual results, and filtering via a threshold, our approach statistically significantly improves on traditional Soundex and n-gram based search techniques used in the search of such texts. Thus, previously unsuccessful searches are now supported.

In the second effort, spam characteristics in peer-to-peer file sharing systems are determined. Using these characteristics, an approach that does not rely on external information or user feedback is developed. Cost reduction techniques are employed resulting in a statistically significant reduction of spam. Thus, the user search experience is improved.

Finally, a novel “self start”, patient-specific approach for the treatment of recurrent urinary tract infections is presented. Using conventional data mining techniques, an approach that improves patient care, reduces bacterial mutation, and lowers treatment cost is presented. Thus, an approach that provides better, in terms of patient comfort, quicker, in terms of outbreak duration, and more economical care for female patients that suffer from recurrent urinary tract infections is described.

Biography Ophir Frieder is the Robert L. McDevitt, K.S.G., K.C.H.S. and Catherine H. McDevitt L.C.H.S. Chair in Computer Science and Information Processing and is Chair of the Department of Computer Science at Georgetown University. His research interests focus on scalable information retrieval systems spanning search and retrieval and communications issues. He is a Fellow of the AAAS, ACM, and IEEE.

February 2, Ahn Jae-wook: Exploratory user interfaces for personalized information access

Personalized information access systems aim to provide tailored information to users according to their various tasks, interests, or contexts. They have long been relied on the ability of algorithms for estimating user interests and generating personalized information. They observe user behaviors, build mental models of the users, and apply the user model for customizing the information. This process can be done even without any explicit user intervention. However, we can add users into the loop of the personalization process, so that the systems can catch user interests even more precisely and the users can flexibly control the behavior of the systems.

In order to exploit the benefits of the user interfaces for personalized information access, we have investigated various aspects of exploratory information access systems. Exploratory information access systems can combine the strengths of algorithms and user interfaces. Users can learn and investigate their information need beyond the simple lookup search strategy. By adding the idea of the exploration to the personalized information access, we could devise advanced user interfaces for the personalization. Specifically, we have tried to understand how we could let users learn, manipulate, and control the core component of many personalized systems, user models. In this presentation, I am going to introduce several ideas about how to present and control user models using different user interfaces. The example studies include open/editable user model, tab-based user model and query control, reference point-based visualization that incorporates the user model and the query spaces, and named-entity based searching/browsing user interface. The results and the lessons of the user studies are discussed.

Bio: Jae-wook Ahn has recently defended his Ph.D. dissertation at the School of Information Sciences, University of Pittsburgh in September 2010. He has worked with his Ph.D. mentor Dr. Peter Brusilovsky and Dr. Daqing He. He is currently a research associate of the Department of Computer Science and the Human Computer Interaction Lab, working with Dr. Ben Shneiderman.

Fall 2010 Speakers

• Roger Levy
• Earl Wagner
• Eugene Charniak
• Dave Newman
• Ray Mooney

October 20, Kristy Hollingshead: Search Errors and Model Errors in Pipeline Systems

Pipeline systems, in which data is sequentially processed in stages with the output of one stage providing input to the next, are ubiquitous in the field of natural language processing (NLP) as well as many other research areas. The popularity of the pipeline system architecture may be attributed to the utility of pipelines in improving scalability by reducing search complexity and increasing efficiency of the system. However, pipelines can suffer from the well-known problem of "cascading errors," where errors earlier in the pipeline propagate to later stages in the pipeline. In this talk I will make a distinction between two different type of cascading errors in pipeline systems. The first I will term "search errors," where there exists a higher-scoring candidate (according to the model), but that candidate has been excluded from the search space. The second type of error that I will address might be termed "model errors," where the highest-scoring candidate (according to the model) is not the best candidate (according to some gold standard). Statistical NLP models are imperfect by nature, resulting in model errors. Interestingly, the same pipeline framework that causes search errors can also resolve (or work around) model errors; in this talk I will demonstrate several techniques for detecting and resolving search and model errors, which can result in improved efficiency with no loss in accuracy. I will briefly mention the technique of pipeline iteration, introduced in my ACL'07 paper, and introduce some related results from my dissertation. I will then focus on work done with my PhD advisor Brian Roark on chart cell constraints, as published in our COLING'08 and NAACL'09 papers; this work provably reduces the complexity of a context-free parser to quadratic performance in the worst case (observably linear) with a slight gain in accuracy using the Charniak parser. While much of this talk will be on parsing pipelines, I am currently extending some of this work to MT pipelines and would welcome discussion along those lines.

Kristy Hollingshead earned her PhD in Computer Science and Engineering this year, from the Center for Spoken Language Understanding (CSLU) at the Oregon Health & Science University (OHSU). She received her B.A. in English-Creative Writing from the University of Colorado in 2000 and her M.S. in Computer Science from OHSU in 2004. Her research interests in natural language processing include parsing, machine translation, evaluation metrics, and assistive technologies. She is also interested in general techniques on improving system efficiency, to allow for richer contextual information to be extracted for use in downstream stages of a pipeline system. Kristy was a National Science Foundation Graduate Research Fellow from 2004-2007.

October 27, Stanley Kok: Structure Learning in Markov Logic Networks

Statistical learning handles uncertainty in a robust and principled way. Relational learning (also known as inductive logic programming) models domains involving multiple relations. Recent years have seen a surge of interest in the statistical relational learning (SRL) community in combining the two, driven by the realization that many (if not most) applications require both and by the growing maturity of the two fields.

Markov logic networks (MLNs) is a statistical relational model that has gained traction within the AI community in recent years because of its robustness to noise and its ability to compactly model complex domains. MLNs combine probability and logic by attaching weights to first-order formulas, and viewing these as templates for features of Markov networks. Learning the structure of an MLN consists of learning both formulas and their weights.

To obtain weighted MLN formulas, we could rely on human experts to specify them. However, this approach is error-prone and requires painstaking knowledge engineering. Further, it will not work on domains where there is no human expert. The ideal solution is to automatically learn MLN structure from data. However, this is a challenging task because of its super-exponential search space. In this talk, we present a series of algorithms that efficiently and accurately learn MLN structure.

November 1, Owen Rambow: Relating Language to Cognitive State

In the 80s and 90s of the last century, in subdisciplines such as planning, text generation, and dialog systems, there was considerable interest in modeling the cognitive states of interacting autonomous agents. Theories such as Speech Act Theory (Austin 1962), the belief-desire-intentions model of Bratman (1987), and Rhetorical Structure Theory (Mann and Thompson 1988) together provide a framework in which to link cognitive state with language use. However, in general natural language processing (NLP), little use was made of such theories, presumably because of the difficulty at the time of some underlying tasks (such as syntactic parsing). In this talk, I propose that it is time to again think about the explicit modeling of cognitive state for participants in discourse. In fact, that is the natural way to formulate what NLP is all about. The perspective of cognitive state can provide a context in which many disparate NLP tasks can be classified and related. I will present two NLP projects at Columbia which relate to the modeling of cognitive state:

Discourse participants need to model each other's cognitive states, and language makes this possible by providing special morphological, syntactic, and lexical markers. I present results in automatically determining the degree of belief of a speaker in the propositions in his or her utterance.

Bio: PhD from University of Pennsylvania, 1994, working on German syntax. My office mate was Philip Resnik. I worked at CoGentex, Inc (a small company) and AT&T Labs -- Research until 2002, and since then at Columbia as a Research Scientist. My research interests cover both the nuts-and-bolts of languages, specifically syntax, and how language is used in context.

November 10, Bob Carpenter: Whence Linguistic Data?

The empirical approach to linguistic theory involves collecting data and annotating it according to a coding standard. The ability of multiple annotators to consistently annotate new data reflects the applicability of the theory. In this talk, I'll introduce a generative probabilistic model of the annotation process for categorical data. Given a collection of annotated data, we can infer the true labels of items, the prevalence of some phenomenon (e.g. a given intonation or syntactic alternation), the accuracy and category bias of each annotator, and the codability of the theory as measured by the mean accuracy and bias of annotators and their variability. Hierarchical model extensions allow us to model item labeling difficulty and take into account annotator background and experience. I'll demonstrate the efficacy of the approach using expert and non-expert pools of annotators for simple linguistic labeling tasks such as textual inference, morphological tagging, and named-entity extraction. I'll discuss applications such as monitoring an annotation effort, selecting items with active learning, and generating a probabilistic gold standard for machine learning training and evaluation.

November 15, William Webber: Information retrieval effectiveness: measurably going nowhere?

Information retrieval works by heuristics; correctness cannot be formally proved, but must be empirically assessed. Test collections make this evaluation automated and repeatable. Collection-based evaluation has been standard for half a century. The IR community prides itself on the rigour of the experimental tradition that has been built upon this foundation; it is notoriously difficult to publish in the field without a thorough experimental validation. No attention, however, has been paid to the question of whether methodological rigour in evaluation has to verifiable. In this talk, we present a survey of retrieval results published over the past decade, which fails to find evidence that retrieval effectiveness is in fact improving. Rather, each experiment's impressive leap forward is preceded by a few careful steps back.

Bio:

William Webber is a Research Associate in the Department of Computer Science and Software Engineering at the University of Melbourne, Australia. He has recently completed his PhD thesis, "Measurement in Information Retrieval Evaluation", under the supervision of Professors Alistair Moffat and Justin Zobel.

December 8: Michael Paul: Summarizing Contrastive Viewpoints in Opinionated Text

Performing multi-document summarization of opinionated text has unique challenges because it is important to recognize that the same information may be presented in different ways from different viewpoints. In this talk, we will present a special kind of contrastive summarization approach intended to highlight this phenomenon and to help users digest conflicting opinions. To do this, we introduce a new graph-based algorithm, Comparative LexRank, to score sentences in a summary based on a combination of both representativeness of the collection and comparability between opposing viewpoints. We then address the issue of how to automatically discover and extract viewpoints from unlabeled text, and we experiment with a novel two-dimensional topic model for the task of unsupervised clustering of documents by viewpoint. Finally, we discuss how these two stages can be combined to both automatically extract and summarize viewpoints in an interesting way. Results are presented on two political opinion data sets.

This project was joint work with ChengXiang Zhai and Roxana Girju.

Bio: Michael Paul is a first-year Ph.D. student of Computer Science at the Johns Hopkins University and a member of the Center for Language and Speech Processing. He earned a B.S. from the University of Illinois at Urbana-Champaign in 2009. He is currently a Graduate Research Fellow of the National Science Foundation and a Dean's Fellow of the Whiting School of Engineering.