Difference between revisions of "Events"

Computational Linguistics and Information Processing

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== 09/05/2012: 5 Minute Madness (Part I) ==
== 09/12/2012: 5 Minute Madness (Part II) ==
== 09/19/2012: Earl Wagner ==
== 10/03/2012: Shay Cohen ==
== 10/10/2012: Beyond MaltParser - Advances in Transition-Based Dependency Parsing ==
== 10/10/2012: Beyond MaltParser - Advances in Transition-Based Dependency Parsing ==
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'''Host:''' Hal Daume III,
'''Host:''' Hal Daume III,
== 10/03/2012: Shay Cohen ==
== 09/19/2012: Earl Wagner ==
== 09/12/2012: 5 Minute Madness (Part II) ==
== 09/05/2012: 5 Minute Madness (Part I) ==
== 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,
== Previous Talks ==
== Previous Talks ==

Revision as of 00:38, 25 August 2012

The CLIP Colloquium is a weekly speaker series organized and hosted by CLIP Lab. The talks are open to everyone. Most talks are held at 11AM in AV Williams 3258 unless otherwise noted. Typically, external speakers have slots for one-on-one meetings with Maryland researchers before and after the talks; contact the host if you'd like to have a meeting.

If you would like to get on the list or for other questions about the colloquium series, e-mail Jimmy Lin, the current organizer.

{{#widget:Google Calendar | |color=B1440E |title=Upcoming Talks |view=AGENDA |height=300 }}

09/05/2012: 5 Minute Madness (Part I)

09/12/2012: 5 Minute Madness (Part II)

09/19/2012: Earl Wagner

10/03/2012: Shay Cohen

10/10/2012: Beyond MaltParser - Advances in Transition-Based Dependency Parsing

Speaker: Joakim Nivre, Uppsala University / Google
Time: Wednesday, October 10, 2012, 11:00 AM
Venue: AVW 3258

Abstract: The transition-based approach to dependency parsing has become popular thanks to its simplicity and efficiency. Systems like MaltParser achieve linear-time parsing with projective dependency trees using locally trained classifiers to predict the next parsing action and greedy best-first search to retrieve the optimal parse tree, assuming that the input sentence has been morphologically disambiguated using a part-of-speech tagger. In this talk, I survey recent developments in transition-based dependency parsing that address some of the limitations of the basic transition-based approach. First, I show how globally trained classifiers and beam search can be used to mitigate error propagation and enable richer feature representations. Secondly, I discuss different methods for extending the coverage to non-projective trees, which are required for linguistic adequacy in many languages.Finally, I present a model for joint tagging and parsing that leads to improvements in both tagging and parsing accuracy as compared to the standard pipeline approach.

About the Speaker: Joakim Nivre is Professor of Computational Linguistics at Uppsala University and currently visiting scientist at Google, New York. He holds a Ph.D. in General Linguistics from the University of Gothenburg and a Ph.D. in Computer Science from Växjö University. Joakim's research focuses on data-driven methods for natural language processing, in particular for syntactic and semantic analysis. He is one of the main developers of the transition-based approach to syntactic dependency parsing, described in his 2006 book Inductive Dependency Parsing and implemented in the MaltParser system. Joakim?s current research interests include the analysis of mildly non-projective dependency structures, the integration of morphological and syntactic processing for richly inflected languages, and methods for cross-framework parser evaluation. He has produced over 150 scientific publications, including 3 books, and has given nearly 70 invited talks at conferences and institutions around the world. He is the current secretary of the European Chapter of the Association for Computational Linguistics.

Host: Hal Daume III,

Previous Talks