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Computational Linguistics and Information Processing

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== 10/10/2012: Beyond MaltParser - Advances in Transition-Based Dependency Parsing ==
 
'''Speaker:''' [http://stp.lingfil.uu.se/~nivre/ Joakim Nivre], Uppsala University / Google<br/>
 
'''Time:''' Wednesday, October 10, 2012, 11:00 AM<br/>
 
'''Venue:''' AVW 3258<br/>
 
  
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, hal@umd.edu
 
  
 
== 10/17/2012: Using Syntactic Head Information in Hierarchical Phrase-Based Translation ==
 
== 10/17/2012: Using Syntactic Head Information in Hierarchical Phrase-Based Translation ==

Revision as of 12:07, 22 October 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 cl-colloquium@umiacs.umd.edu list or for other questions about the colloquium series, e-mail Jimmy Lin, the current organizer.


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10/17/2012: Using Syntactic Head Information in Hierarchical Phrase-Based Translation

Speaker: Junhui Li
Time: Wednesday, October 17, 2012, 11:00 AM
Venue: AVW 3258

The traditional hierarchical phrase-based (HPB) model is prone to overgeneration due to lack of linguistic knowledge: the grammar may suggest more derivations than appropriate, many of which may lead to ungrammatical translations. On the other hand, limitations of glue grammar rules in HPB model may actually prevent systems from considering some reasonable derivations. This talk presents a simple but effective translation model, called the Head-Driven HPB (HD-HPB) model, which incorporates head information in translation rules to better capture syntax-driven information in a derivation. In addition, unlike the original glue rules, the HD-HPB model allows improved reordering between any two neighboring non-terminals to explore a larger reordering search space. In experiments, we examined different head label sets to refine non-terminal X, including part-of-speech (POS) tags, coarsed POS tags, dependency labels.

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.

10/23/2012: Bootstrapping via Graph Propagation

Speaker: Anoop Sarkar, Simon Fraser University
Time: Tuesday, October 23, 2012, 2:00 PM
Venue: AVW 4172

Note special time and place!!!

In natural language processing, the bootstrapping algorithm introduced by David Yarowsky (15 years ago) is a discriminative unsupervised learning algorithm that uses some seed rules to bootstrap a classifier (this is the ordinary sense of bootstrapping which is distinct from the Bootstrap in statistics). The Yarowsky algorithm works remarkably well on a wide variety of NLP classification tasks such as distinguishing between word senses and deciding if a noun phrase is an organization, location, or person.

Extending previous attempts at providing an objective function optimization view of Yarowsky, we show that bootstrapping a classifier from a small set of seed rules can be viewed as the propagation of labels between examples via features shared between them. This paper introduces a novel variant of the Yarowsky algorithm based on this view. It is a bootstrapping learning method which uses a graph propagation algorithm with a well defined per-iteration objective function that incorporates the cautious behaviour of the original Yarowsky algorithm.

The experimental results show that our proposed bootstrapping algorithm achieves state of the art performance or better on several different natural language data sets, outperforming other unsupervised methods such as the EM algorithm. We show that cautious learning is an important principle in unsupervised learning, however we do not understand it well, and we show that the Yarowsky algorithm can outperform or match co-training without any reliance on multiple views.

About the Speaker: Anoop Sarkar is an Associate Professor at Simon Fraser University in British Columbia, Canada where he co-directs the Natural Language Laboratory. He received his Ph.D. from the Department of Computer and Information Sciences at the University of Pennsylvania under Prof. Aravind Joshi for his work on semi-supervised statistical parsing using tree-adjoining grammars.

His research is focused on statistical parsing and machine translation (exploiting syntax or morphology, semi-supervised learning, and domain adaptation). His interests also include formal language theory and stochastic grammars, in particular tree automata and tree-adjoining grammars.

10/31/2012: Kilian Weinberger

Previous Talks