PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Analysis of joint inference strategies for the semantic role labeling of Spanish and Catalan
Mihai Surdeanu, Roser Morante and LLuís Màrquez
In: Computational Linguistics and Intelligent Text Processing: 9th International Conference, CICLing 2008 Lecture Notes in Computer Science , 4919/2008 . (2008) Springer , Berlin/Heidelberg , pp. 206-218. ISBN 978-3540781349


This paper analyzes two joint inference approaches for semantic role labeling: re-ranking of candidate semantic frames generated by one local model and combination of two distinct models at argument-level using meta learning. We perform an empirical analysis on two recently released corpora of annotated semantic roles in Spanish and Catalan. This work yields several novel conclusions: (a) the proposed joint inference strategies yield good results even under adverse conditions: small training corpora, only two individual models available for combination, minimal output available from the individual models; (b) stacking of the two joint inference approaches is successful, which indicates that the two inference models provide complementary benefits. Our results are currently the best for the identification of semantic role for Spanish and Catalan.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:5740
Deposited By:Roser Morante
Deposited On:08 March 2010