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Computational Linguistics and Information Processing

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== Colloquia ==
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''Titles and abstracts appear after the calendar.''
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== CLIP Colloquium ==
  
=== Google Calendar for CLIP Speakers===
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The CLIP Colloquium is a weekly speaker series organized and hosted by CLIP Lab. The talks are open to everyone. Most talks are held on Wednesday at 11AM in AV Williams 3258 unless otherwise noted. Typically, external speakers have slots for one-on-one meetings with Maryland researchers.
  
{{#widget:Google Calendar
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If you would like to get on the clip-talks@umiacs.umd.edu list or for other questions about the colloquium series, e-mail [mailto:oard@umiacs.umd.edu Doug Oard], the current organizer.
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=== 2010 Past Speakers (prior to this page going live) ===
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For up-to-date information, see the [https://talks.cs.umd.edu/lists/7 UMD CS Talks page].  (You can also subscribe to the calendar there.)
  
* Roger Levy
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=== Colloquium Recordings ===
* Earl Wagner
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* [[Colloqium Recording (Fall 2020)|Fall 2020]]
* Eugene Charniak
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* [[Colloqium Recording (Spring 2021)|Spring 2021]]
* Dave Newman
 
* Ray Mooney
 
  
=== October 20, Kristy Hollingshead: Search Errors and Model Errors in Pipeline Systems ===
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=== Previous Talks ===
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* [[https://talks.cs.umd.edu/lists/7?range=past Past talks, 2013 - present]]
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* [[CLIP Colloquium (Spring 2012)|Spring 2012]]  [[CLIP Colloquium (Fall 2011)|Fall 2011]]  [[CLIP Colloquium (Spring 2011)|Spring 2011]]  [[CLIP Colloquium (Fall 2010)|Fall 2010]]
  
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.
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== CLIP NEWS  ==
  
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.
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* News about CLIP researchers on the UMIACS website [http://www.umiacs.umd.edu/about-us/news]
 
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* Please follow us on Twitter @umdclip [https://twitter.com/umdclip?lang=en]
=== 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 ===
 
 
 
=== 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.
 
 
 
=== November 24: Ned Talley ===
 

Revision as of 18:21, 6 June 2021

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CLIP Colloquium

The CLIP Colloquium is a weekly speaker series organized and hosted by CLIP Lab. The talks are open to everyone. Most talks are held on Wednesday at 11AM in AV Williams 3258 unless otherwise noted. Typically, external speakers have slots for one-on-one meetings with Maryland researchers.

If you would like to get on the clip-talks@umiacs.umd.edu list or for other questions about the colloquium series, e-mail Doug Oard, the current organizer.

For up-to-date information, see the UMD CS Talks page. (You can also subscribe to the calendar there.)

Colloquium Recordings

Previous Talks

CLIP NEWS

  • News about CLIP researchers on the UMIACS website [1]
  • Please follow us on Twitter @umdclip [2]