DeSRL: A linear-time semantic role labeling system

Massimiliano Ciaramita, Giuseppe Attardi, Felice Dell'Orletta, Mihai Surdeanu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

This paper describes the DeSRL system, a joined effort of Yahoo! Research Barcelona and Università di Pisa for the CoNLL-2008 Shared Task (Surdeanu et al., 2008). The system is characterized by an efficient pipeline of linear complexity components, each carrying out a different sub-task. Classifier errors and ambiguities are addressed with several strategies: revision models, voting, and reranking. The system participated in the closed challenge ranking third in the complete problem evaluation with the following scores: 82.06 labeled macro F1 for the overall task, 86.6 labeled attachment for syntactic dependencies, and 77.5 labeled F1 for semantic dependencies.

Original languageEnglish (US)
Title of host publicationCoNLL 2008 - Proceedings of the Twelfth Conference on Computational Natural Language Learning
Pages258-262
Number of pages5
StatePublished - Dec 1 2008
Externally publishedYes
Event12th Conference on Computational Natural Language Learning, CoNLL 2008 - Manchester, United Kingdom
Duration: Aug 16 2008Aug 17 2008

Publication series

NameCoNLL 2008 - Proceedings of the Twelfth Conference on Computational Natural Language Learning

Other

Other12th Conference on Computational Natural Language Learning, CoNLL 2008
CountryUnited Kingdom
CityManchester
Period8/16/088/17/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

Fingerprint Dive into the research topics of 'DeSRL: A linear-time semantic role labeling system'. Together they form a unique fingerprint.

Cite this