CLIP Colloquium (Spring 2012) - Revision history

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== Summer 2012 Speakers ==

=== June 14: Practice Talks ===

'''NOTE UNUSUAL TIME: 1PM (Same place)'''

* 13:00 Jordan: Besting the Quiz Master: Crowdsourcing Incremental Classification Games
* 13:30 Jags : Regularized Interlingual Projections: Evaluation on Multilingual Transliteration
* 14:00 An : SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations
* 14:30 Jordan: Topic Models for Dynamic Translation Model Adaptation
* 14:50 Yuening: Efficient Tree-Based Topic Modeling
* 15:10 Ke : Modeling Images using Transformed Indian Buffet Processes
* 15:30 Michael: Thesis defense practice talk

== Spring 2012 Speakers ==

=== May 16: Dan Goldwasser: Towards Natural Instructions based Machine Learning ===

'''NOTE UNUSUAL TIME: 3PM (Same place)'''

Machine learning is traditionally formalized and researched as the study of learning concepts and decision functions from labeled examples. We are interested in providing an alternative way of communicating knowledge to an automated system, by allowing a human teacher to interact with an automated learner using natural language instructions, thus allowing the teacher to communicate the relevant domain expertise to the learner without necessarily knowing anything about the internal representations used in the learning process. The process of learning a decision function is therefore viewed as a natural language lesson interpretation problem instead of learning from labeled examples. The lesson interpretation problem, framed as a structure prediction problem, is typically approached using supervised machine learning techniques which are often as costly and difficult as learning the original decision function.

In this talk I will discuss how to approach this learning problem without direct supervision, and present learning protocols which rely on indirect supervision originating from evaluating the learner's performance on the final concept taught. This learning scenario which relies on the connection between the two learning tasks, can be generally applied to other learning problems in natural language processing. I will also discuss how to model this problem more broadly and show results in several natural language processing domains, such as textual entailment, transliteration and semantic parsing.

=== May 9: Planning for Fall ===

We'll discuss the future of the CLIP colloquium. On the agenda:
* Whom to invite
* How to run the colloquium
* When to have the colloquium

=== May 2: Jacob Eisenstein: Searching for social meanings in social media ===

Social interaction is increasingly conducted through online
platforms such as Facebook and Twitter, leaving a recorded trace of
millions of individual interactions. While some have focused on the
supposed deficiencies of social media with respect to more traditional
communication channels, language in social media features the same
rich connections with personal and group identity, style, and social
context. However, social media's unique set of linguistic affordances
causes social meanings to be expressed in new and perhaps surprising
ways. This talk will describe research that builds on large-scale
social media corpora using analytic tools from statistical machine
learning. I will focus on some of the ways in which social media data
allow us to go beyond traditional sociolinguistic methods, but I will
also discuss lessons from the sociolinguistics literature that the
new generation of "big data" research might do well to heed.

This research includes collaborations with David Bamman, Brendan
O'Connor, Tyler Schnoebelen, Noah A. Smith, and Eric P. Xing.

Bio: Jacob Eisenstein is an Assistant Professor in the School of
Interactive Computing at Georgia Tech. He works on statistical natural
language processing, focusing on social media analysis, discourse, and
non-verbal communication. Jacob was a Postdoctoral researcher at
Carnegie Mellon and the University of Illinois. He completed his
Ph.D. at MIT in 2008, winning the George M. Sprowls dissertation
award.

=== April 25: Youngjoong Ko: A Wikipedia Based Query Translation and Expansion Technique for Cross-Language Information Retrieval ===
Translation resources, such as bilingual word lists, parallel and
comparable corpora and machine translation systems, are important
factors affecting the effectiveness of cross-language information
retrieval systems. However, fully developed machine translation
systems, and even topic-appropriate parallel and comparable corpora,
will sometimes not be available. Therefore, I will present a CLIR system that uses Wikipedia as a bilingual resource for query
translation and expansion in this presentation. Bilingual word lists including bilingual pairs, same-language synonymy sets based on redirect pages, and
polysemy sets based on disambiguation pages, are first automatically
extracted from Wikipedia. The bilingual word lists are then used as a
basis for query translation, and a concept link graph is automatically
created from Wikipedia link information for use in query expansion
using a random walk algorithm. Evaluation results on the NTCIR-5
English-Korean test collection indicate significant improvements over
systems that use bilingual word lists extracted from machine-readable
dictionaries and overall results that are comparable to that of
monolingual retrieval.

Bio: Youngjoong Ko is an associate professor of Computer Engineering at Dong-A University in Korea. He received his PhD in 2003 at Sogang University. His research focuses on Information Retrieval (CLIR, text classification/summarization), Opinion Mining (comparative mining, aspect/sentiment classification), Topic Modeling, and Dialogue System (speech-act analysis, dialogue modeling). He is currently at CLIP laboratory of UMIACS in University of Maryland as a visiting scholar.

=== April 18: Amit Goyal, Streaming and Sketch Algorithms for Large Data NLP ===

Streaming and sketch algorithms provide a memory-efficient solution to
an average desktop (8 GB of memory) user to exploit large data. At the
same time, these algorithms can easily be implemented in distributed
setting and hence provide memory- and time-efficient solution for
commodity cluster users. The availability of large and rich examples
of text data is due to the emergence of the World Wide Web, social
media and mobile devices. Such vast data sets have led to leaps in the
performance of many language-based tasks: the concept is that simple
models trained on big data can outperform more complex models with
fewer examples. Streaming and sketch algorithms process the large
data sets in one pass and represent a large data set with a compact
summary, typically much smaller than the full size of the input.
However, the memory and time savings come at the expense of
approximate solutions; though I will demonstrate that approximate
solutions achieved on large data are comparable to exact solutions on
large data, and outperform exact solutions on smaller data.

First, I show the empirical effectiveness of approximate streaming
language models on Statistical Machine Translation task. Second, I
demonstrate a version of the Count-Min sketch accurately solves three
large-scale NLP problems using small bounded memory footprint. Third,
I conduct a systematic study and compare many `sketch' algorithms that
approximate count of items with focus on large-scale NLP tasks.
Finally, I will talk about my future work on constructing large
nearest-neighbor graphs in a memory- and time-efficient manner.

=== April 11: Jordan Boyd-Graber, Mr. LDA: A Flexible Large Scale Topic Modeling Package using Variational Inference in MapReduce ===

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for
exploring document collections. Because of the increasing prevalence of large
datasets, there is a need to improve the scalability of inference for LDA. In
this paper, we introduce a novel and flexible large scale topic modeling
package in MapReduce (Mr. LDA). As opposed to other techniques which use Gibbs
sampling, our proposed framework uses variational inference, which easily fits
into a distributed environment. More importantly, this variational
implementation, unlike highly tuned and specialized implementations based on
Gibbs sampling, is easily extensible. We demonstrate two extensions of the
models possible with this scalable framework: informed priors to guide topic
discovery and extracting topics from a multilingual corpus. We compare the
scalability of Mr. LDA against Mahout, an existing large scale topic modeling
package.

This is a practice talk for WWW 2012.

=== April 4: Doug Oard, Evaluating E-Discovery Search: The TREC Legal Track ===

Civil litigation in this the USA relies on each side making relevant evidence available
to the other, a process known as "discovery." The explosive growth of information in
digital form has led to an increasing focus on how search technology can best be applied
to balance costs and responsiveness in what has come to be known as "e-discovery".
This is now a multi-billion dollar business, one in which new vendors are entering the
market frequently, usually with impressive claims about the efficacy of their products
or services. Courts, attorneys, and companies are actively looking to understand what
should constitute best practice, both in the design of search technology and in how
that technology is employed. In this talk I will provide an overview of the e-discovery
process, and then I will use that background to motivate a discussion of which aspects
of that process the TREC Legal Track is seeking to model. I will then spend most of the
talk describing two novel aspects of evaluation design: (1) recall-focused evaluation in
large collections, and (2) modeling an interactive process for "responsive review" with
fairly high fidelity. Although I will draw on the results of participating teams to illustrate
what we have learned, my principal focus will be on discussing what we presently
understand to be the strengths and weaknesses of our evaluation designs.

Bio: Douglas Oard is a Professor at the University of Maryland, College Park, with joint
appointments in the College of Information Studies and the Institute for Advanced
Computer Studies, where he currently serves as director of the Computational Linguistics
and Information Processing lab. He earned his Ph.D. in Electrical Engineering from
the University of Maryland. Dr. Oard’s research interests center around the use of
emerging technologies to support information seeking by end users. His recent work
has focused on interactive techniques for cross-language information retrieval, searching
conversational media such as speech and email, evaluation design for e-discovery in the
TREC Legal Track, and support for sense-making in large digital archival collections.
Additional information is available at http://terpconnect.umd.edu/~oard/.

=== March 28: Noah Smith, Linguistic Structure Prediction with AD3 ===

In this talk, I will present AD3 (Alternating Directions Dual Decomposition), an algorithm for approximate MAP inference in loopy graphical models with discrete random variables, including structured prediction problems. AD3 is simple to implement and well-suited to problems with hard constraints expressed in first-order logic. It often finds the exact MAP solution, giving a certificate when it does; when it doesn’t, it can be embedded within an exact branch and bound technique. I’ll show experimental results on two natural language processing tasks, dependency parsing and frame-semantic parsing. This work was done in collaboration with Andre Martins, Dipanjan Das, Pedro Aguiar, Mario Figueiredo, and Eric Xing.

Bio: Noah Smith is the Finmeccanica Associate Professor of Language Technologies and Machine Learning in the School of Computer Science at Carnegie Mellon University. He received his Ph.D. in Computer Science, as a Hertz Foundation Fellow, from Johns Hopkins University in 2006 and his B.S. in Computer Science and B.A. in Linguistics from the University of Maryland in 2001. His research interests include statistical natural language processing, especially unsupervised methods, machine learning for structured data, and applications of natural language processing. His book, Linguistic Structure Prediction, covers many of these topics. He serves on the editorial board of the journal Computational Linguistics and the Journal of Artificial Intelligence Research and received a best paper award at the ACL 2009 conference. His research group, Noah's ARK, is supported by the NSF (including an NSF CAREER award), DARPA, Qatar NRF, IARPA, ARO, Portugal FCT, and gifts from Google, HP Labs, IBM Research, and Yahoo Research.

=== March 14: Michael Bloodgood, Stopping Active Learning based on Stabilizing Predictions ===

Abstract: How do you know when to stop annotating additional training data?
The marginal value of additional data can vary a lot.
In the context of active learning, where one is trying to optimally select
training data so as to minimize annotation cost, I will discuss methods for
determining when to stop the annotation process. I'll propose a method based
on stabilizing predictions; give empirical results for this approach;
and provide analytical explanation for understanding its behavior.

Bio: Michael Bloodgood is a researcher at the Center for Advanced Study of Language.

=== March 12: George Foster, Instance Weighting meets MIRA: Domain Adaptation in Statistical Machine Translation via Phrase-Level Features ===

In previous work on SMT domain adaptation (Foster et al, EMNLP 2010), we
demonstrated the effectiveness of weighting phrase pairs according to
generality and domain relevance. In this talk I will describe recent work that
extends this approach in two ways. First, rather than assuming a substantial
amount of in-domain material and an undifferentiated out-of-domain corpus, we
consider a more general setting with an in-domain development set but otherwise
heterogeneous training material. Second, rather than adjusting phrase-pair
counts using maximum likelihood in order to circumvent MERT limitations, we
incorporate adaptive features directly into the log-linear model, and tune with
MIRA. I will give empirical results for this approach, and compare them to
strong mixture-model baselines.

Bio: George Foster is a senior researcher at the National Research Council of
Canada. He is on the board of AMTA, and the editorial boards of Computational
Linguistics and Machine Translation. His research has mainly focused on
applications for translation technology, beginning with tools for translators,
and evolving as statistical MT has become more viable. His doctoral work led to
the TransType project on interactive MT via sentence completion. In 2003 he led
a JHU CLSP workshop on confidence estimation for SMT. More recently he has
worked on adaptation and discriminative estimation. George is the proud
originator of the "desperation-based MT" technique, first deployed by the
University of Montreal for the NIST 2003 evaluation, and widely adopted by
participants in subsequent evaluations.

=== March 7: Slav Petrov, Fast, Accurate and Robust Multilingual Syntactic Analysis ===

To build computer systems that can 'understand' natural language, we
need to go beyond bag-of-words models and take the grammatical
structure of language into account. Part-of-speech tag sequences and
dependency parse trees are one form of such structural analysis that
is easy to understand and use.
This talk will cover three topics. First, I will present a
coarse-to-fine architecture for dependency parsing that uses
linear-time vine pruning and structured prediction cascades. The
resulting pruned third-order model is twice as fast as an unpruned
first-order model and compares favorably to a state-of-the-art
transition-based parser in terms of speed and accuracy. I will then
present a simple online algorithm for training structured prediction
models with extrinsic loss functions. By tuning a parser with a loss
function for machine translation reordering, we can show that parsing
accuracy matters for downstream application quality, producing
improvements of more than 1 BLEU point on an end-to-end machine
translation task. Finally, I will present approaches for projecting
part-of-speech taggers and syntactic parsers across language
boundaries, allowing us to build models for languages with no labeled
training data. Our projected models significantly outperform
state-of-the-art unsupervised models and constitute a first step
towards an universal parser.

This is joint work with Ryan McDonald, Keith Hall, Dipanjan Das,
Alexander Rush, Michael Ringgaard and Kuzman Ganchev (a.k.a. the
Natural Language Parsing Team at Google).

Bio: Slav Petrov is a Senior Research Scientist in Google's New York
office. He works on problems at the intersection of natural language
processing and machine learning. He is in particular interested in
syntactic parsing and its applications to machine translation and
information extraction. He also teaches a class on Statistical Natural
Language Processing at New York University every Fall.

Prior to Google, Slav completed his PhD degree at UC Berkeley, where
he worked with Dan Klein. He holds a Master's degree from the Free
University of Berlin, and also spent a year as an exchange student at
Duke University. Slav was a member of the FU-Fighters team that won
the RoboCup 2004 world championship in robotic soccer and recently won
a best paper award at ACL 2011 for his work on multilingual syntactic
analysis.

Slav grew up in Berlin, Germany, but is originally from Sofia,
Bulgaria. He therefore considers himself a Berliner from Bulgaria.
Whenever Bulgaria plays Germany in soccer, he supports Bulgaria.

=== February 29: Vlad Eidelman, Unsupervised Textual Analysis with Rich Features ===

Learning how to properly partition a set of documents into categories
in an unsupervised manner is quite challenging, since documents are
inherently multidimensional, and a given set of documents can be
correctly partitioned along a number of dimensions, depending
on the criterion. Since the partition criterion for a supervised model
is encoded in the data via the class labels, even the standard
information retrieval representation of a document as a vector of term
frequencies is sufficient for many state-of-the-art classification
models. This representation is especially well suited for the most
common application: topic (or thematic) analysis, where term presence
is highly indicative of class. Furthermore, for tasks where term
presence may not be adequate, such as sentiment or perspective
analysis, discriminative models have the ability to incorporate
complex features, allowing them to generalize and adapt to the
specific domain. In the case where we do not have access to resources
for supervised training, we must turn to unsupervised clustering
models. Clustering models rely almost exclusively on a simple
bag-of-words vector representation, which performs well for topic
analysis, but unfortunately, is not guaranteed to perform well for a
different task.

In this talk, I will present a feature-enhanced unsupervised model for
categorizing textual data. The presented model allows for the
integration of arbitrary features of the observations within a
document. While in generative models the observed context is usually a
single unigram, or bigram, our model can robustly expand the context
to extract features from a block of text of larger size. After
presenting the model derivation, I will describe the use of complex
automatically derived linguistic and statistical features across three
practical tasks with different criterion: perspective, sentiment, and
topic analysis. I show that by introducing domain relevant features,
we can guide the model towards the task-specific partition we want to
learn. For each task, our feature enhanced model outperforms strong
baselines and state-of-the-art models.

Bio: Vladimir Eidelman is a fourth-year Ph.D. student in the
Department of Computer Science at the University of Maryland, working
primarily with Philip Resnik. He received his B.S. in Computer Science
and Philosophy from Columbia University in 2008 and a M.S in Computer
Science from UMD in 2010. His research interests are in machine
learning and natural language processing problems, such as machine
translation, structured prediction, and unsupervised learning. He is
the recipient of the National Science Foundation Graduate Research and
National Defense Science and Engineering Graduate Fellowships.

=== Feb 22: Rebecca Hwa, The Role of Machine Translation in Modeling English as a Second Language (ESL) Writings ===

Much of the English text on the web is written by people whose native
language is not English. Many English as a Second Language (ESL)
writers, even those with a high level of proficiency, make common
grammatical mistakes. In this talk, I will present some of our recent
work on modeling ESL writings. I will first talk about characterizing
ESL students’ writing revision process with syntax-driven machine
translation models. Then, I will focus on modeling similarities in ESL
writers’ preposition selection mistakes using a distance metric
learning algorithm.

BIO: Rebecca Hwa is an Associate Professor in the Department of Computer
Science at the University of Pittsburgh. Before joining Pitt, she was
a postdoc at University of Maryland. She received her PhD in Computer
Science from Harvard University in 2001 and her B.S. in Computer
Science and Engineering from UCLA in 1993. Dr. Hwa's primary research
interests include multilingual processing, machine translation, and
semi-supervised learning methods. She is a recipient of the NSF CAREER
Award. Her work has also been supported by NIH and DARPA. Dr. Hwa is a
past chair of the executive board of the North American Chapter of the
Association for Computational Linguistics.

=== Feb 15: Lidan Wang, Learning to Efficiently Rank (AVW 3258) ===

Technological advances have led to increases in the types and amounts of data, and there is a great need for developing methods
to manage and find relevant information from such data to satisfy user's information needs. Learning to rank is an emerging
discipline at the intersection of machine learning, data mining, and information retrieval. It develops principled machine
learning algorithms to construct ranking (i.e., retrieval) models, for finding and ranking relevant information to user queries
over large amounts of data. Although learning to rank approaches are capable of learning highly effective ranking functions, they
have mostly ignored the important issue of model efficiency (i.e., model speed). Given that efficiency and effectiveness are
competing forces that often counteract each other, models that are optimized for effectiveness alone may not meet the strict
efficiency requirements when dealing with real-world large-scale datasets.

My Ph.D. thesis introduces the Learning to Efficiently Rank framework for learning large-scale ranking models that facilitate
fast and effective retrieval, by exploiting and optimizing the tradeoffs between model complexity (i.e., speed) and accuracy. At
a basic level, this framework learns ranking models whose speed and accuracy can be explicitly controlled. I proposed and
designed solutions for three problems within this framework: 1) learning large-scale ranking models according to a desired
tradeoff between model speed and accuracy; 2) constructing temporally-constrained models capable of returning results under time
budgets; 3) breaking through the speed/accuracy tradeoff barrier by developing a novel cascade ranking model, and learning the
cascade model structure and parameters with a novel boosting-based learning algorithm. My research extends the conventional
effectiveness-centric approach in model learning and takes an efficiency-minded look at building effective retrieval models.
Results show that models learned this way significantly outperform traditional machine-learned models in terms of speed without
sacrificing result effectiveness. Moreover, the new models work particularly well when users impose stringent time requirements
for ranked retrieval on very large datasets.

Bio: I am a final-year Ph.D. student in the Computer Science Department at the University of Maryland, College Park. My main research interests are in information retrieval, machine learning, and text mining, with an emphasis on efficient and scalable methods for managing, learning, and retrieving information from large-scale data. I have a Master's degree from the Computer Science Department, University of Wisconsin, Madison, and a Bachelor's degree from the Computer Science and Engineering Department, University of Florida. My primary advisor is Prof. Jimmy Lin.

=== Feb 8: Janyce Wiebe, Subjectivity and Sentiment Analysis: from Words to Discourse (AVW 3258) ===

There is growing interest in the automatic extraction of opinions,
emotions, and sentiments in text (subjectivity analysis) to support
natural language processing applications, ranging from mining product
reviews and summarization, to automatic question answering and
information extraction. In this talk, I will describe work on tasks in
subjectivity analysis along a continuum: from subjectivity word sense
labeling through discourse-level opinion interpretation and stance
recognition.

Bio: Janyce Wiebe is Professor of Computer Science and Director of the
Intelligent Systems Program at the University of Pittsburgh. Her
research with students and colleagues has been in discourse
processing, pragmatics, and word-sense disambiguation. A major
concentration of her research is "subjectivity analysis", recognizing
and interpretating expressions of opinions and sentiments in text, to
support NLP applications such as question answering, information
extraction, text categorization, and summarization. Her professional
roles have included ACL Program Co-Chair, NAACL Program Chair, NAACL
Executive Board member, Computational Linguistics and Language
Resources and Evaluation Editorial Board member, AAAI Workshop
Co-Chair, ACM Special Interest Group on Artificial Intelligence
(SIGART) Vice-Chair, and ACM-SIGART/AAAI Doctoral Consortium Chair.

← Older revision		Revision as of 00:35, 25 August 2012
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	~~== Summer 2012 Speakers ==~~		== June 14: Practice Talks ==

	=== June 14: Practice Talks ===

	'''NOTE UNUSUAL TIME: 1PM (Same place)'''		'''NOTE UNUSUAL TIME: 1PM (Same place)'''
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	* 15:30 Michael: Thesis defense practice talk		* 15:30 Michael: Thesis defense practice talk

	~~== Spring 2012 Speakers ==~~		== May 16: Dan Goldwasser: Towards Natural Instructions based Machine Learning ==

	=== May 16: Dan Goldwasser: Towards Natural Instructions based Machine Learning ===

	'''NOTE UNUSUAL TIME: 3PM (Same place)'''		'''NOTE UNUSUAL TIME: 3PM (Same place)'''
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	In this talk I will discuss how to approach this learning problem without direct supervision, and present learning protocols which rely on indirect supervision originating from evaluating the learner's performance on the final concept taught. This learning scenario which relies on the connection between the two learning tasks, can be generally applied to other learning problems in natural language processing. I will also discuss how to model this problem more broadly and show results in several natural language processing domains, such as textual entailment, transliteration and semantic parsing.		In this talk I will discuss how to approach this learning problem without direct supervision, and present learning protocols which rely on indirect supervision originating from evaluating the learner's performance on the final concept taught. This learning scenario which relies on the connection between the two learning tasks, can be generally applied to other learning problems in natural language processing. I will also discuss how to model this problem more broadly and show results in several natural language processing domains, such as textual entailment, transliteration and semantic parsing.

	=== May 9: Planning for Fall ===		== May 9: Planning for Fall ==

	We'll discuss the future of the CLIP colloquium. On the agenda:		We'll discuss the future of the CLIP colloquium. On the agenda:
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	* When to have the colloquium		* When to have the colloquium

	=== May 2: Jacob Eisenstein: Searching for social meanings in social media ===		== May 2: Jacob Eisenstein: Searching for social meanings in social media ==

	Social interaction is increasingly conducted through online		Social interaction is increasingly conducted through online
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	award.		award.

	=== April 25: Youngjoong Ko: A Wikipedia Based Query Translation and Expansion Technique for Cross-Language Information Retrieval ===		== April 25: Youngjoong Ko: A Wikipedia Based Query Translation and Expansion Technique for Cross-Language Information Retrieval ==

	Translation resources, such as bilingual word lists, parallel and		Translation resources, such as bilingual word lists, parallel and
	comparable corpora and machine translation systems, are important		comparable corpora and machine translation systems, are important
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	Bio: Youngjoong Ko is an associate professor of Computer Engineering at Dong-A University in Korea. He received his PhD in 2003 at Sogang University. His research focuses on Information Retrieval (CLIR, text classification/summarization), Opinion Mining (comparative mining, aspect/sentiment classification), Topic Modeling, and Dialogue System (speech-act analysis, dialogue modeling). He is currently at CLIP laboratory of UMIACS in University of Maryland as a visiting scholar.		Bio: Youngjoong Ko is an associate professor of Computer Engineering at Dong-A University in Korea. He received his PhD in 2003 at Sogang University. His research focuses on Information Retrieval (CLIR, text classification/summarization), Opinion Mining (comparative mining, aspect/sentiment classification), Topic Modeling, and Dialogue System (speech-act analysis, dialogue modeling). He is currently at CLIP laboratory of UMIACS in University of Maryland as a visiting scholar.

	=== April 18: Amit Goyal, Streaming and Sketch Algorithms for Large Data NLP ===		== April 18: Amit Goyal, Streaming and Sketch Algorithms for Large Data NLP ==

	Streaming and sketch algorithms provide a memory-efficient solution to		Streaming and sketch algorithms provide a memory-efficient solution to
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	nearest-neighbor graphs in a memory- and time-efficient manner.		nearest-neighbor graphs in a memory- and time-efficient manner.

	=== April 11: Jordan Boyd-Graber, Mr. LDA: A Flexible Large Scale Topic Modeling Package using Variational Inference in MapReduce ===		== April 11: Jordan Boyd-Graber, Mr. LDA: A Flexible Large Scale Topic Modeling Package using Variational Inference in MapReduce ==

	Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for		Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for
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	This is a practice talk for WWW 2012.		This is a practice talk for WWW 2012.

	=== April 4: Doug Oard, Evaluating E-Discovery Search: The TREC Legal Track ===		== April 4: Doug Oard, Evaluating E-Discovery Search: The TREC Legal Track ==

	Civil litigation in this the USA relies on each side making relevant evidence available		Civil litigation in this the USA relies on each side making relevant evidence available
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	Additional information is available at http://terpconnect.umd.edu/~oard/.		Additional information is available at http://terpconnect.umd.edu/~oard/.

	=== March 28: Noah Smith, Linguistic Structure Prediction with AD3 ===		== March 28: Noah Smith, Linguistic Structure Prediction with AD3 ==

	In this talk, I will present AD3 (Alternating Directions Dual Decomposition), an algorithm for approximate MAP inference in loopy graphical models with discrete random variables, including structured prediction problems. AD3 is simple to implement and well-suited to problems with hard constraints expressed in first-order logic. It often finds the exact MAP solution, giving a certificate when it does; when it doesn’t, it can be embedded within an exact branch and bound technique. I’ll show experimental results on two natural language processing tasks, dependency parsing and frame-semantic parsing. This work was done in collaboration with Andre Martins, Dipanjan Das, Pedro Aguiar, Mario Figueiredo, and Eric Xing.		In this talk, I will present AD3 (Alternating Directions Dual Decomposition), an algorithm for approximate MAP inference in loopy graphical models with discrete random variables, including structured prediction problems. AD3 is simple to implement and well-suited to problems with hard constraints expressed in first-order logic. It often finds the exact MAP solution, giving a certificate when it does; when it doesn’t, it can be embedded within an exact branch and bound technique. I’ll show experimental results on two natural language processing tasks, dependency parsing and frame-semantic parsing. This work was done in collaboration with Andre Martins, Dipanjan Das, Pedro Aguiar, Mario Figueiredo, and Eric Xing.
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	Bio: Noah Smith is the Finmeccanica Associate Professor of Language Technologies and Machine Learning in the School of Computer Science at Carnegie Mellon University. He received his Ph.D. in Computer Science, as a Hertz Foundation Fellow, from Johns Hopkins University in 2006 and his B.S. in Computer Science and B.A. in Linguistics from the University of Maryland in 2001. His research interests include statistical natural language processing, especially unsupervised methods, machine learning for structured data, and applications of natural language processing. His book, Linguistic Structure Prediction, covers many of these topics. He serves on the editorial board of the journal Computational Linguistics and the Journal of Artificial Intelligence Research and received a best paper award at the ACL 2009 conference. His research group, Noah's ARK, is supported by the NSF (including an NSF CAREER award), DARPA, Qatar NRF, IARPA, ARO, Portugal FCT, and gifts from Google, HP Labs, IBM Research, and Yahoo Research.		Bio: Noah Smith is the Finmeccanica Associate Professor of Language Technologies and Machine Learning in the School of Computer Science at Carnegie Mellon University. He received his Ph.D. in Computer Science, as a Hertz Foundation Fellow, from Johns Hopkins University in 2006 and his B.S. in Computer Science and B.A. in Linguistics from the University of Maryland in 2001. His research interests include statistical natural language processing, especially unsupervised methods, machine learning for structured data, and applications of natural language processing. His book, Linguistic Structure Prediction, covers many of these topics. He serves on the editorial board of the journal Computational Linguistics and the Journal of Artificial Intelligence Research and received a best paper award at the ACL 2009 conference. His research group, Noah's ARK, is supported by the NSF (including an NSF CAREER award), DARPA, Qatar NRF, IARPA, ARO, Portugal FCT, and gifts from Google, HP Labs, IBM Research, and Yahoo Research.

	=== March 14: Michael Bloodgood, Stopping Active Learning based on Stabilizing Predictions ===		== March 14: Michael Bloodgood, Stopping Active Learning based on Stabilizing Predictions ==

	Abstract: How do you know when to stop annotating additional training data?		Abstract: How do you know when to stop annotating additional training data?
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	Bio: Michael Bloodgood is a researcher at the Center for Advanced Study of Language.		Bio: Michael Bloodgood is a researcher at the Center for Advanced Study of Language.

	=== March 12: George Foster, Instance Weighting meets MIRA: Domain Adaptation in Statistical Machine Translation via Phrase-Level Features ===		== March 12: George Foster, Instance Weighting meets MIRA: Domain Adaptation in Statistical Machine Translation via Phrase-Level Features ==

	In previous work on SMT domain adaptation (Foster et al, EMNLP 2010), we		In previous work on SMT domain adaptation (Foster et al, EMNLP 2010), we
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	participants in subsequent evaluations.		participants in subsequent evaluations.

	=== March 7: Slav Petrov, Fast, Accurate and Robust Multilingual Syntactic Analysis ===		== March 7: Slav Petrov, Fast, Accurate and Robust Multilingual Syntactic Analysis ==

	To build computer systems that can 'understand' natural language, we		To build computer systems that can 'understand' natural language, we
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	Whenever Bulgaria plays Germany in soccer, he supports Bulgaria.		Whenever Bulgaria plays Germany in soccer, he supports Bulgaria.

	=== February 29: Vlad Eidelman, Unsupervised Textual Analysis with Rich Features ===		== February 29: Vlad Eidelman, Unsupervised Textual Analysis with Rich Features ==

	Learning how to properly partition a set of documents into categories		Learning how to properly partition a set of documents into categories
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	National Defense Science and Engineering Graduate Fellowships.		National Defense Science and Engineering Graduate Fellowships.

	=== Feb 22: Rebecca Hwa, The Role of Machine Translation in Modeling English as a Second Language (ESL) Writings ===		== Feb 22: Rebecca Hwa, The Role of Machine Translation in Modeling English as a Second Language (ESL) Writings ==

	Much of the English text on the web is written by people whose native		Much of the English text on the web is written by people whose native
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	Association for Computational Linguistics.		Association for Computational Linguistics.

	=== Feb 15: Lidan Wang, Learning to Efficiently Rank (AVW 3258) ===		== Feb 15: Lidan Wang, Learning to Efficiently Rank (AVW 3258) ==

	Technological advances have led to increases in the types and amounts of data, and there is a great need for developing methods		Technological advances have led to increases in the types and amounts of data, and there is a great need for developing methods
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	=== Feb 8: Janyce Wiebe, Subjectivity and Sentiment Analysis: from Words to Discourse (AVW 3258) ===		== Feb 8: Janyce Wiebe, Subjectivity and Sentiment Analysis: from Words to Discourse (AVW 3258) ==

	There is growing interest in the automatic extraction of opinions,		There is growing interest in the automatic extraction of opinions,