CLIP Colloquium (Spring 2013)
Computational Linguistics and Information Processing
Revision as of 23:48, 25 October 2013 by Jimmylin
01/30/2013: Human Translation and Machine Translation
Speaker: Philipp Koehn, University of Edinburgh
Time: Wednesday, January 30, 2013, 11:00 AM
Venue: AVW 3258
Despite all the recent successes of machine translation, when it comes to high quality publishable translation, human translators are still unchallenged. Since we can't beat them, can we help them to become more productive? I will talk about some recent work on developing assistance tools for human translators. You can also check out a prototype here and learn about our ongoing European projects CASMACAT and MATECAT.
About the Speaker: Philipp Koehn is Professor of Machine Translation at the School of Informatics at the University of Edinburgh, Scotland. He received his PhD at the University of Southern California and spent a year as postdoctoral researcher at MIT. He is well-known in the field of statistical machine translation for the leading open source toolkit Moses, the organization of the annual Workshop on Statistical Machine Translation and its evaluation campaign as well as the Machine Translation Marathon. He is founding president of the ACL SIG MT and currently serves a vice president-elect of the ACL SIG DAT. He has published over 80 papers and the textbook in the field. He manages a number of EU and DARPA funded research projects aimed at morpho-syntactic models, machine learning methods and computer assisted translation tools.
02/06/2013: A New Recommender System for Large-scale Document Exploration
Speaker: Chong Wang, Carnegie Mellon University
Time: Wednesday, February 6, 2013, 11:00 AM
Venue: AVW 3258
How can we help people quickly navigate the vast amount of data and acquire useful knowledge from it? Recommender systems provide a promising solution to this problem. They narrow down the search space by providing a few recommendations that are tailored to users' personal preferences. However, these systems usually work like a black box, limiting further opportunities to provide more exploratory experiences to their users.
In this talk, I will describe how we build a new recommender system for document exploration. Specially, I will talk about two building blocks of the system in detail. The first is about a new probabilistic model for document recommendation that is both predictive and interpretable. It not only gives better predictive performance, but also provides better transparency than traditional approaches. This transparency creates many new opportunities for exploratory analysis---For example, a user can manually adjust her preferences and the system responds to this by changing its recommendations. Second, building a recommender system like this requires learning the probabilistic model from large-scale empirical data. I will describe a scalable approach for learning a wide class of probabilistic models that include our recommendation model as a special case.
About the Speaker: Chong is a Project Scientist in Eric Xing's group, Machine Learning Department, Carnegie Mellon University. His PhD advisor was David M. Blei from Princeton University.
02/13/2013: Computational Modeling of Sociopragmatic Language Use in Arabic and English Social Media
Speaker: Mona Diab, Columbia University
Time: Wednesday, February 13, 2013, 11:00 AM
Venue: AVW 3258
Social media language is a treasure trove for mining and understanding human interactions. In discussion fora, people naturally form groups and subgroups aligning along points of consensus and contention. These subgroup formations are quite nuanced as people could agree on some topic such as liking the movie the matrix, but some within that group might disagree on rating the acting skills of Keanu Reeves. Languages manifest these alignments exploiting interesting sociolinguistic devices in different ways. In this talk, I will present our work on subgroup modeling and detection in both Arabic and English social media language. I will share with you our experiences with modeling both explicit and implicit attitude using high and low dimensional feature modeling. This work is the beginning of an interesting exploration into the realm of building computational models of some aspects of the sociopragmatics of human language with the hopes that this research could lead to a better understanding of human interaction.
About the Speaker: Mona Diab is an Associate Professor of Computer Science at the George Washington University. She is also a cofounder of the CADIM (Columbia Arabic Dialect Modeling) group at Columbia University. Mona earned her PhD in Computational Linguistics from University of Maryland College Park with Philip Resnik in 2003 and then did her postdoctoral training with Daniel Jurafsky at Stanford University where she was part of the NLP group. from 2005 till 2012, before joining GWU in Jan of 2013, Mona held the position of Research Scientist/Principle Investigator at Columbia University Center for Computational Learning Systems (CCLS). Mona's research interests span computational lexical semantics, multilingual processing (with a special interest in Arabic and low resource languages), unsupervised learning for NLP, computational sociopragmatic modeling, information extraction and machine translation. Over the past 9 years, Mona has developed significant expertise in modeling low resource languages with a focus on Arabic dialect processing. She is especially interested in ways to leverage existing rich resources to inform algorithms for processing low resource languages. Her research has been published in over 90 papers in various internationally recognized scientific venues. Mona serves as the current elected President of the ACL SIG on Semitic Language Processing, she is also the elected Secretary for the ACL SIG on issues in the Lexicon (SIGLEX). She also serves on the NAACL board as an elected member. Mona recently (2012) co-founded the yearly *SEM conference that attempts to bring together all aspects of semantic processing under the same umbrella venue.
02/14/2013: Efficient Probabilistic Models for Rankings and Orderings
Speaker: Jon Huang, Stanford University
Time: Thursday, February 14, 2013, 11:00 AM
Venue: AVW 3258
The need to reason probabilistically with rankings and orderings arises in a number of real world problems. Probability distributions over rankings and orderings arise naturally, for example, in preference data, and political election data, as well as a number of less obvious settings such as topic analysis and neurodegenerative disease progression modeling. Representing distributions over the space of all rankings is challenging, however, due to the factorial number of ways to rank a collection of items. The focus of my talk is to discuss methods for combatting this factorial explosion in probabilistic representation and inference.
Ordinarily, a typical machine learning method for dealing with combinatorial complexity might be to exploit conditional independence relations in order to decompose a distribution into compact factors of a graphical model. For ranked data, however, a far more natural and useful probabilistic relation is that of `riffled independence'. I will introduce the concept of riffled independence and discuss how these riffle independent relations can be used to decompose a distribution over rankings into a product of compactly represented factors. These so-called hierarchical riffle-independent distributions are particularly amenable to efficient inference and learning algorithms and in many cases lead to intuitively interpretable probabilistic models. To illustrate the power of exploiting riffled independence, I will discuss a few applications, including Irish political election analysis, visualizing the japanese preferences of sushi types and modeling the progression of Alzheimer's disease, showing results on real datasets in each problem.
This is joint work with Carlos Guestrin (University of Washington), Ashish Kapoor (Microsoft Research) and Daniel Alexander (University College London).
02/27/2013: Building Scholarly Methodologies with Large-Scale Topic Analysis
Speaker: David Mimno, Princeton University
Time: Wednesday, February 27, 2013, 9:00 AM
Venue: Hornbake (South Wing) Room 2119
NOTE SPECIAL TIME AND LOCATION!!!
In the last ten years we have seen the creation of massive digital text collections, from Twitter feeds to million-book libraries, all in dozens of languages. At the same time, researchers have developed text mining methods that go beyond simple word frequency analysis to uncover thematic patterns. When we combine big data with powerful algorithms, we enable analysts in many different fields to enhance qualitative perspectives with quantitative measurements. But these methods are only useful if we can apply them at massive scale and distinguish consistent patterns from random variations. In this talk I will describe my work building reliable topic modeling methodologies for humanists, social scientists and science policy officers.
About the Speaker: David Mimno is a postdoctoral researcher in the Computer Science department at Princeton University. He received his PhD from the University of Massachusetts, Amherst. Before graduate school, he served as Head Programmer at the Perseus Project, a digital library for cultural heritage materials, at Tufts University. He is supported by a CRA Computing Innovation fellowship.
03/13/2013: Is Any Politics Local? An Automated Analysis of Mayoral and Gubernatorial Addresses
Speaker: Dan Hopkins, Georgetown University
Time: Wednesday, March 13, 2013, 11:00 AM
Venue: AVW 3258
Dubbed "laboratories of democracy," America's states and its large cities face a wide variety of public policy challenges. But in a period of expanding federal authority and increased long-distance communication, the extent to which U.S. states and large cities pursue varying policy agendas is at once important and unknown. This paper draws on techniques from automated content analysis to measure the major topics in more than 500 "State of the State" and "State of the City" addresses given by American executive officials since 2000. Drawing on the Correlated Topic Model (Blei and Lafferty 2006) and other approaches to topic modeling, it demonstrates that big-city mayors do address a distinctive set of topics from their counterparts in state capitols, but one that is surprisingly consistent across cities. Knowing a mayor's political party provides little leverage on the topics he or she is likely to highlight, while the same is true for objective indicators such as economic conditions or the city's crime rate. At the state level, partisanship proves more predictive of the topics addressed by Governors, but there, too, institutional responsibilities constrain leaders to emphasize a broad and similar set of issues. American political institutions inscribe a substantial role for geographic and institutional differences, but the policy agendas of America's states and largest cities are homogeneous and overlapping.
About the Speaker: Daniel J. Hopkins is an Assistant Professor of Government at Georgetown University whose research focuses on American politics, with a special emphasis on political behavior, urban and local politics, racial and ethnic politics, and statistical methods. Specifically, his research has addressed issues including the role of rhetoric and of local contexts in shaping political behavior. It has also involved the development and application of automated techniques for analyzing political rhetoric. Professor Hopkins' work has appeared in a variety of scholarly and popular outlets, including the American Political Science Review, the American Journal of Political Science, the Journal of Politics, and The Washington Post. Professor Hopkins received his Ph.D. from Harvard University in 2007.
03/27/2013: Corpora and Statistical Analysis of Non-Linguistic Symbol Systems
Speaker: Richard Sproat, Google New York
Time: Wednesday, March 27, 2013, 11:00 AM
Venue: AVW 3258
We report on the creation and analysis of a set of corpora of non-linguistic symbol systems. The resource, the first of its kind, consists of data from seven systems, both ancient and modern, with four further systems under development, and several others planned. The systems represent a range of types, including heraldic systems, formal systems, and systems that are mostly or purely decorative. We also compare these systems statistically with a large set of linguistic systems, which also range over both time and type.
We show that none of the measures proposed in published work by Rao and colleagues (Rao et al., 2009a; Rao, 2010) or Lee and colleagues (Lee et al., 2010a) works. In particular, Rao’s entropic measures are evidently useless when one considers a wider range of examples of real non-linguistic symbol systems. And Lee’s measures, with the cutoff values they propose, misclassify nearly all of our non-linguistic systems. However, we also show that one of Lee’s measures, with different cutoff values, as well as another measure we develop here, do seem useful. We further demonstrate that they are useful largely because they are both highly correlated with a rather trivial feature: mean text length.
About the Speaker: Richard Sproat received his Ph.D. in Linguistics from the Massachusetts Institute of Technology in 1985. He has worked at AT&T Bell Labs, at Lucent's Bell Labs and at AT&T Labs -- Research, before joining the faculty of the University of Illinois. From there he moved to the Center for Spoken Language Understanding at the Oregon Health & Science University. In the Fall of 2012 he moved to Google, New York as a Research Scientist.
Sproat has worked in numerous areas relating to language and computational linguistics, including syntax, morphology, computational morphology, articulatory and acoustic phonetics, text processing, text-to-speech synthesis, and text-to-scene conversion. Some of his recent work includes multilingual named entity transliteration, the effects of script layout on readers' phonological awareness, and tools for automated assessment of child language. At Google he works on multilingual text normalization and finite-state methods for language processing. He also has a long-standing interest in writing systems and symbol systems more generally.
04/10/2013: Learning with Marginalized Corrupted Features
Speaker: Kilian Weinberger, Washington University in St. Louis
Time: Wednesday, April 10, 2013, 11:00 AM
Venue: AVW 3258
If infinite amounts of labeled data are provided, many machine learning algorithms become perfect. With finite amounts of data, regularization or priors have to be used to introduce bias into a classifier. We propose a third option: learning with marginalized corrupted features. We (implicitly) corrupt existing data as a means to generate additional, infinitely many, training samples from a slightly different data distribution -- this is computationally tractable, because the corruption can be marginalized out in closed form. Our framework leads to machine learning algorithms that are fast, generalize well and naturally scale to very large data sets. We showcase this technology as regularization for general risk minimization and for marginalized deep learning for document representations. We provide experimental results on part of speech tagging as well as document and image classification.
About the Speaker: Kilian Q. Weinberger is an Assistant Professor in the Department of Computer Science & Engineering at Washington University in St. Louis. He received his Ph.D. from the University of Pennsylvania in Machine Learning under the supervision of Lawrence Saul. Prior to this, he obtained his undergraduate degree in Mathematics and Computer Science at the University of Oxford. During his career he has won several best paper awards at ICML, CVPR and AISTATS. In 2011 he was awarded the AAAI senior program chair award and in 2012 he received the NSF CAREER award. Kilian Weinberger's research is in Machine Learning and its applications. In particular, he focuses on high dimensional data analysis, metric learning, machine learned web-search ranking, transfer- and multi-task learning as well as bio medical applications.
04/17/2013: Recursive Deep Learning in Natural Language Processing and Computer Vision
Speaker: Richard Socher, Stanford University
Time: Wednesday, April 17, 2013, 11:00 AM
Venue: AVW 3258
Hierarchical and recursive structure is commonly found in different modalities, including natural language sentences and scene images. I will introduce several recursive deep learning models that, unlike standard deep learning methods can learn compositional meaning vector representations for phrases or images.
These recursive neural network based models obtain state-of-the-art performance on a variety of syntactic and semantic language tasks such as parsing, sentiment analysis, paraphrase detection and relation classification for extracting knowledge from the web. Because often no language specific assumptions are made the same architectures can be used for visual scene understanding and object classification from 3d images.
Besides the good performance, the models capture interesting phenomena in language such as compositionality. For instance the models learn that “not good” has worse sentiment than “good” or that high level negation can change the meaning of longer phrases with many positive words. Furthermore, unlike most machine learning approaches that rely on human designed feature sets, features are learned as part of the model.
About the Speaker: Richard Socher is a PhD student at Stanford working with Chris Manning and Andrew Ng. His research interests are machine learning for NLP and vision. He is interested in developing new models that learn useful features, capture compositional and hierarchical structure in multiple modalities and perform well across multiple tasks. He was awarded the 2011 Yahoo! Key Scientific Challenges Award, the Distinguished Application Paper Award at ICML 2011 and a Microsoft Research PhD Fellowship in 2012.
05/01/2013: Probabilistic Soft Logic, Stephen Bach
In this talk, we will give an overview of probabilistic soft logic (PSL), a tool being developed in the LINQS group at UMD for modeling, learning, and inference in structured and multi-relational domains. We'll describe the basic syntax and semantics for the language and then describe the underlying mathematical framework upon which efficient inference and learning is built. We refer to the underlying mathematical model as a hinge-loss Markov random field (HL-MRF). HL-MRFs have a number of nice properties, including the fact that most probable explanation (MPE) inference corresponds to a convex optimization problem. We present recent results showing that, using state–of-the-art optimization techniques, we can perform inference on problems with tens of thousands of random variables in seconds, and problems with hundreds of thousands of random variables in minutes. We are currently working on several approaches for distributed inference in PSL, which promise even greater scalability. We will conclude by discussing applications of PSL to problems such as: group identification in social media, activity recognition in videos, image reconstruction, knowledge graph identification, schema mapping, drug target prediction, and others as time permits.
05/08/2013: The Foreseer: Integrative Retrieval and Mining of Information in Online Communities
Speaker: Qiaozhu Mei, University of Michigan
Time: Wednesday, May 8, 2013, 11:00 AM
Venue: AVW 3258
With the growth of online communities, the Web has evolved from networks of shared documents into networks of knowledge-sharing groups and individuals. A vast amount of heterogeneous yet interrelated information is being generated, making existing information analysis techniques inadequate. Current data mining tools often neglect the actual context, creators, and consumers of information. Foreseer is a user-centric framework for the next generation of information retrieval and mining for online communities. It represents a new paradigm of information analysis through the integration of the four “C’s”: content, context, crowd, and cloud.
In this talk, we will introduce our recent effort of integrative analysis and mining of information in online communities. We will highlight the real world problems in online communities to which the Foreseer techniques have been successfully applied. These topics include the identification of information needs from social media, the prediction of the adoption of hashtags in microblogging communities, and the prediction of social lending behaviors in microfinance communities.
About the Speaker: Qiaozhu Mei is an assistant professor at the School of Information, the University of Michigan. He is widely interested in information retrieval, text mining, natural language processing and their applications in web search, social computing, and health informatics. He has served in the program committee of almost all major conferences in these areas. He is also a recipient of the NSF CAREER Award, two runner-up best student paper awards at KDD, and a SIGKDD dissertation award.