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Computational Linguistics and Information Processing

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== Colloquia ==
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<center>[[Image:colloq.jpg|center|504px|x]]</center>
  
''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.
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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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== Spring 2011 Speakers ==
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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.)
  
=== February 16, Ophir Frieder: Humane Computing ===
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=== Colloquium Recordings ===
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* [[Colloqium Recording (Fall 2020)|Fall 2020]]
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* [[Colloqium Recording (Spring 2021)|Spring 2021]]
  
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.
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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]]
  
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.
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== CLIP NEWS ==
  
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.
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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]
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.
 

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]