Natural language research announcements

["Alexandre Klementiev" <klementi AT uiuc.edu>]

Dear faculty and students,

Dr. Xavier Carreras will give a talk (details below) at the AIIS
 seminar this Thursday.  There will be a student meeting with Xavier at 1pm  in Siebel 3403; if you would like to meet with him personally, please let me know.

Thank you,



Alex



Title: Full Syntactic Parsing with Dynamic Programming and the Perceptron


Speaker: Xavier Carreras, MIT





Date: May 1, 4:00pm
Location:    Siebel 3405






Abstract:

I will describe an efficient, expressive model for natural language
parsing that makes use of the perceptron algorithm in conjunction with
dynamic programming search. An advantage of dynamic programming
approaches is that a very large set of possible parsing structures can
be considered for a given sentence. Plus, the discriminative nature of
our model allows a great deal of flexibility in representing candidate
parse trees for a sentence, including PCFG-based features, dependency
relations and features on the surface of the sentence.

A critical problem when training a discriminative model for a
task of the scale of natural language parsing is the efficiency of the
parsing algorithm involved. Discriminative training algorithms, such as
the averaged perceptron, require to repeatedly parse the training set
under the model. But factors such as the sentence length or the number
of syntactic labels in the parses, which directly affect the efficiency
of the algorithms, are usually very large in real parsing tasks, making
exact inference virtually intractable. As a result, in spite of the
potential advantages of discriminative parsing methods, there has been
little previous work for full constituent parsing without the use of
fairly severe restrictions.

I will show that training models for full constituent parsing
is possible, using a formalism closely related to Tree Adjoining
Grammars that can be efficiently parsed with dependency parsing
algorithms. Key to the efficiency of our approach is the use of a
lower-order model for pruning the space of full parsing structures, and
that performs remarkably well. Experiments on parsing the Penn WSJ
treebank show that the model gives state-of-the-art accuracy.

Joint work with Michael Collins and Terry Koo.

Bio:


Xavier Carreras is a postdoctoral fellow at MIT CSAIL. He studied
computer science at the Technical University of Catalonia in Barcelona,
where he received the PhD in Artificial Intelligence in 2005. His
research interests are in natural language processing and machine
learning, and he has worked in syntactic parsing, semantic role
labeling and named entity extraction, among other tasks.