CLIP Colloquium (Fall 2012) - Revision history

Jimmylin at 19:43, 11 January 2013

2013-01-11T19:43:36Z

← Older revision		Revision as of 19:43, 11 January 2013
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	'''About the Speaker:''' Junhui Li joined CLIP lab as a post-doc researcher from Aug 2012. He was previously a post-doc researcher in the Centre for Next Generation Localisation (CNGL), at Dublin City University from Feb 2011 to Jul 2012. Before that, he was a student at NLP Lab of Soochow University, China.		'''About the Speaker:''' Junhui Li joined CLIP lab as a post-doc researcher from Aug 2012. He was previously a post-doc researcher in the Centre for Next Generation Localisation (CNGL), at Dublin City University from Feb 2011 to Jul 2012. Before that, he was a student at NLP Lab of Soochow University, China.

			== 11/28/2012: New Machine Learning Tools for Structured Prediction ==

			'''Speaker:''' [http://old-site.clsp.jhu.edu/~ves/ Veselin Stoyanov], Johns Hopkins University<br/>
			'''Time:''' Wednesday, November 28, 2012, 2012, 11:00 AM<br/>
			'''Venue:''' AVW 3258<br/>

			I am motivated by structured prediction problems in NLP and social network analysis. Markov Random Fields (MRFs) and other Probabilistic Graphical Models (PGMs) are suitable for representing structured prediction: they can model joint distributions and utilize standard inference procedures. MRFs also provide a principled ways for incorporating background knowledge and combining multiple systems.

			Two properties of structured prediction problems make learning challenging. First, structured prediction almost inevitably requires approximation to inference, decoding or model structure. Second, unlike the traditional ML setting that assumes i.i.d. training and test data, structured learning problems often consist of a single example used both for training and prediction.

			We address the two issues above. First, we argue that the presence of approximations in MRF-based systems requires a novel perspective on training. Instead of maximizing data likelihood, one should seek the parameters that minimize the empirical risk of the entire imperfect system. We show how to locally optimize this risk using error back-propagation and local optimization. On four NLP problems our approach significantly reduces loss on test data compared to choosing approximate MAP parameters.

			Second, we utilize data imputation in the limited data setting. At test time we use sampling to impute data that is a more accurate approximation of the data distribution. We use our risk minimization techniques to train fast discriminative models on the imputed data. This we can: (i) train discriminative models given a single training and test example; (ii) train generative/discriminative hybrids that can incorporate useful priors and learn from semi-supervised data.

			'''About the Speaker:''' Veselin Stoyanov is currently a postdoctoral researcher at the Human Language Technology Center of Excellence (HLT-COE) at Johns Hopkins University (JHU). He will be joining Facebook as a Research Scientist starting in January 2013. Previously he spent two years working with Prof. Jason Eisner at JHU's Center for Language and Speech Processing supported by a Computing Innovation Postdoctoral Fellowship. He received the Ph.D. degree from Cornell University under the supervision of Prof. Claire Cardie in 2009 and the Honors B.Sc. from the University of Delaware in 2002. His research interests reside in the intersection of Machine Learning and Computational Linguistics. More precisely, he is interested in using probabilistic models for complex structured problems with applications to knowledge base population, modeling social networks, extracting information from text and coreference resolution. In addition to the CIFellowship, Ves Stoyanov is the recipient of an NSF Graduate Research Fellowship and other academic honors.

			== 12/05/2012: Combining Statistical Translation Techniques for Cross-Language Information Retrieval ==

			'''Speaker:''' [http://www.cs.umd.edu/~fture/Home.html Ferhan Ture], University of Maryland<br/>
			'''Time:''' Wednesday, December 5, 2012, 11:00 AM<br/>
			'''Venue:''' AVW 3258<br/>

			Cross-language information retrieval today is dominated by techniques
			that rely principally on context-independent token-to-token mappings
			despite the fact that state-of-the-art statistical machine translation
			systems now have far richer translation models available in their
			internal representations. This paper explores combination-of-evidence
			techniques using three types of statistical translation models:
			context-independent token translation, token translation using
			phrase-dependent contexts, and token translation using
			sentence-dependent contexts. Context-independent translation is
			performed using statistically-aligned tokens in parallel text,
			phrase-dependent translation is performed using aligned statistical
			phrases, and sentence-dependent translation is performed using those
			same aligned phrases together with an n-gram language model.
			Experiments on retrieval of Arabic, Chinese, and French documents
			using English queries show that no one technique is optimal for all
			queries, but that statistically significant improvements in mean
			average precision over strong baselines can be achieved by combining
			translation evidence from all three techniques. The optimal
			combination is, however, found to be resource-dependent, indicating
			a need for future work on robust tuning to the characteristics of
			individual collections.

			This is a practice talk for COLING 2012.

Jimmylin at 12:39, 25 November 2012

2012-11-25T12:39:13Z

@@ Line 38: / Line 38: @@
 == 09/05/2012: 5 Minute Madness (Part I) ==
 == 09/12/2012: 5 Minute Madness (Part II) ==
 == 09/19/2012: CoB: Pairwise Similarity on Large Text Collections with MapReduce==
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 '''Host:''' Hal Daume III, hal@umd.edu

Jimmylin at 21:55, 23 October 2012

2012-10-23T21:55:16Z

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 == 09/19/2012: CoB: Pairwise Similarity on Large Text Collections with MapReduce==
 '''Speaker:''' Earl Wagner, University of Maryland<br/>
 '''Time:''' Wednesday, September 19, 2012, 11:00 AM<br/>
@@ Line 49: / Line 50: @@
 '''About the Speaker:''' Earl J. Wagner is a Postdoctoral Research Associate at the University of Maryland, College Park in the College of Information Studies (Maryland's iSchool). He was previously a Research Assistant at Northwestern University where he earned his Ph.D. in Computer Science.
 == 09/26/2012: Better! Faster! Stronger (theorems)! Learning to Balance Accuracy and Efficiency when Predicting Linguistic Structures ==
@@ Line 83: / Line 83: @@
 of "what goes wrong when I try to apply off-the-shelf machine
 learning models to real language processing problems?"

Jimmylin at 22:48, 26 September 2012

2012-09-26T22:48:01Z

← Older revision		Revision as of 22:48, 26 September 2012
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	== 09/12/2012: 5 Minute Madness (Part II) ==		== 09/12/2012: 5 Minute Madness (Part II) ==

			== 09/19/2012: CoB: Pairwise Similarity on Large Text Collections with MapReduce==
			'''Speaker:''' Earl Wagner, University of Maryland<br/>
			'''Time:''' Wednesday, September 19, 2012, 11:00 AM<br/>
			'''Venue:''' AVW 3258<br/>

			Faced with high-volume information streams, intelligence analysts often rely on standing queries to retrieve materials that they need to see. Results of these queries are currently extended by effective and efficient probabilistic techniques that find similar, non-matching content. We discuss research looking further afield to find additional useful documents via MapReduce techniques performing rapid clustering of documents. This approach is intended to provide an improved “peripheral vision” to overcome some blind spots, yielding both immediate utility (detection of documents that otherwise would not have been found) and the potential for improvements to specific standing queries.

			'''About the Speaker:''' Earl J. Wagner is a Postdoctoral Research Associate at the University of Maryland, College Park in the College of Information Studies (Maryland's iSchool). He was previously a Research Assistant at Northwestern University where he earned his Ph.D. in Computer Science.


			== 09/26/2012: Better! Faster! Stronger (theorems)! Learning to Balance Accuracy and Efficiency when Predicting Linguistic Structures ==
			'''Speaker:''' Hal Daume III, University of Maryland<br/>
			'''Time:''' Wednesday, September 26, 2012, 11:00 AM<br/>
			'''Venue:''' AVW 3258<br/>

			Viewed abstractly, many classic problems in natural language
			processing can be cast as trying to map a complex input (eg., a
			sequence of words) to a complex output (eg., a syntax tree or
			semantic graph). This task is challenging both because language
			is ambiguous (learning difficulties) and represented with
			discrete combinatorial structures (computational difficulties).
			I will describe my multi-pronged research effort to develope
			learning algorithms that explicitly learn to trade-off accuracy
			and efficiency, applied to a variety of language processing
			phenomena. Moreover, I will show that in some cases, we can
			actually obtain model that is faster and more accurate by
			exploiting smarter learning algorithms. And yes, those
			algorithms come with stronger theoretical guarantees too.

			The key insight that makes this possible is a connection between
			the task of predicting structured objects (what I care about) and
			imitation learning (a subfield in robotics). This insight came
			about as a result of my work a few years ago, and has formed the
			backbone of much of my work since then. These connections have
			led other NLP and robotics researchers to make their own
			independent advances using many of these ideas.

			At the end of the talk, I'll briefly survey some of my other
			contributions in the areas of domain adaptation and multilingual
			modeling, both of which also fall under the general rubric
			of "what goes wrong when I try to apply off-the-shelf machine
			learning models to real language processing problems?"

Jimmylin at 18:18, 14 September 2012

2012-09-14T18:18:05Z

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	__NOTOC__		__NOTOC__

	== 08/20/2012: TopSig – Signature Files Revisited ==		== 08/20/2012: TopSig – Signature Files Revisited ==
	'''Speaker:''' Shlomo Geva, Queensland University of Technology, Australia<br/>		'''Speaker:''' Shlomo Geva, Queensland University of Technology, Australia<br/>

Jimmylin at 18:17, 14 September 2012

2012-09-14T18:17:50Z

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 == 08/20/2012: TopSig – Signature Files Revisited ==
 '''Speaker:''' Shlomo Geva, Queensland University of Technology, Australia<br/>
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 '''Host:''' Doug Oard, oard@umd.edu

Jimmylin: Created page with '== 08/20/2012: TopSig – Signature Files Revisited == '''Speaker:''' Shlomo Geva, Queensland University of Technology, Australia
'''Time:''' Monday, August 20, 2012, 11:00 ...'

2012-08-25T00:37:55Z

Created page with '== 08/20/2012: TopSig – Signature Files Revisited == '''Speaker:''' Shlomo Geva, Queensland University of Technology, Australia<br/> '''Time:''' Monday, August 20, 2012, 11:00 ...'

New page

== 08/20/2012: TopSig – Signature Files Revisited ==
'''Speaker:''' Shlomo Geva, Queensland University of Technology, Australia<br/>
'''Time:''' Monday, August 20, 2012, 11:00 AM<br/>
'''Venue:''' AVW 2120<br/>

'''Abstract:''' Performance comparisons between File Signatures and Inverted
Files for text retrieval have previously shown several
significant shortcomings of file signatures relative to inverted
files. The inverted file approach underpins most state-of-the-art
search engine algorithms, such as Language and Probabilistic
models. It has been widely accepted that traditional
file signatures are inferior alternatives to inverted files. This
paper describes TopSig, a modern approach to the construction
of file signatures - many advances in semantic hashing and
dimensionality reduction have been made in recent times,
but these were not so far linked to general purpose, signature
file based, search engines. This paper introduces a different
signature file approach that builds upon and extends
these recent advances. We are able to demonstrate significant
improvements in the performance of signature file based
indexing and retrieval, performance that is comparable to
that of state of the art inverted file based systems, including
Language models and BM25. These findings suggest that
file signatures offer a viable alternative to inverted files in
suitable settings and position the file signature model in
the class of Vector Space retrieval models.
TopSig is an open-source search engine from QUT and it can be discussed too if there is an interest.

'''About the Speaker:''' Associate Professor Shlomo Geva is the discipline leader for
Computational Intelligence and Signal Processing in the Computer
Science Department at the Queensland University of Technology in
Brisbane, Australia. His research interests include clustering,
cross-language information retrieval, focused information retrieval,
link discovery, and xml indexing.

'''Host:''' Doug Oard, oard@umd.edu