Using semantic cues to learn syntax
Name
Barzilay-Using semantic cues to learn syntax.pdf
Size
160.58 KB
Format
Adobe PDF
Checksum (MD5)
9b43419f90ac06ffceb6066cd8c18f86
Author(s)
Naseem, Tahira
Barzilay, Regina
Date Issued
August 2011
Journal
Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Naseem, Tahira and Regina Barzilay."Using semantic cues to learn syntax." Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, Hyatt Regency San Francisco, August 7–11, 2011, USA.
Version
Author's final manuscript
Abstract
We present a method for dependency grammar induction that utilizes sparse annotations of semantic relations. This induction set-up is attractive because such annotations provide useful
clues about the underlying syntactic structure, and they are readily available in many domains (e.g., info-boxes and HTML markup). Our method is based on the intuition that syntactic realizations of the same semantic predicate exhibit some degree of consistency. We incorporate this intuition in
a directed graphical model that tightly links the syntactic and semantic structures. This design enables us to exploit syntactic regularities while still allowing for variations. Another strength of the model lies in its ability to capture non-local dependency relations. Our results demonstrate that even a small amount of semantic annotations greatly improves the accuracy of learned dependencies when tested on both in-domain and out-of-domain texts.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3741/3975
