Semantic role labeling as sequential tagging

Lluís Màrquez, Pere Comas, Jesús Giménez, Neus Català

Research output: Contribution to conferencePaper

29 Citations (Scopus)

Abstract

In this paper we present a semantic role labeling system submitted to the CoNLL- 2005 shared task. The system makes use of partial and full syntactic information and converts the task into a sequential BIO-tagging. As a result, the labeling architecture is very simple. Building on a state-of-the-art set of features, a binary classifier for each label is trained using AdaBoost with fixed depth decision trees. The final system, which combines the outputs of two base systems performed F 1=76.59 on the official test set. Additionally, we provide results comparing the system when using partial vs. full parsing nput information.

Original languageEnglish
Pages193-196
Number of pages4
Publication statusPublished - 1 Dec 2005
Event9th Conference on Computational Natural Language Learning, CoNLL 2005 - Ann Arbor, MI, United States
Duration: 29 Jun 200530 Jun 2005

Other

Other9th Conference on Computational Natural Language Learning, CoNLL 2005
CountryUnited States
CityAnn Arbor, MI
Period29/6/0530/6/05

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ASJC Scopus subject areas

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

Cite this

Màrquez, L., Comas, P., Giménez, J., & Català, N. (2005). Semantic role labeling as sequential tagging. 193-196. Paper presented at 9th Conference on Computational Natural Language Learning, CoNLL 2005, Ann Arbor, MI, United States.