Sequential SRL Using Selectional Preferences. An aproach with Maximum Entropy Markov Models
by
Beñat Zapirain
,
Eneko Agirre
,
Donostia Basque Country
| Citations: | 1 - 1 self |
BibTeX
@MISC{Zapirain_sequentialsrl,
author = {Beñat Zapirain and Eneko Agirre and Donostia Basque Country},
title = {Sequential SRL Using Selectional Preferences. An aproach with Maximum Entropy Markov Models},
year = {}
}
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Abstract
We present a sequential Semantic Role Labeling system that describes the tagging problem as a Maximum Entropy Markov Model. The system uses full syntactic information to select BIO-tokens from input data, and classifies them sequentially using state-of-the-art features, with the addition of Selectional Preference features. The system presented achieves competitive performance in the CoNLL-2005 shared task dataset and it ranks first in the SRL subtask of the Semeval-2007 task 17. 1
Citations
| 1996 | WordNet: An Electronic Lexical Database - Fellbaum - 1998 |
| 466 | Automatic Labeling of Semantic Roles - Gildea, Jurafsky - 2002 |
| 256 | Kingsbury (2005) The Proposition Bank: an annotated corpus of semantic roles - Martha, Gildea, et al. |
| 35 | Learning classto-class selectional preferences - Agirre, Martinez - 2001 |
| 25 | Combination strategies for semantic role labeling - Surdeanu, Carreras, et al. - 2007 |
| 9 | Hierarchical recognition of propositional arguments with perceptrons - Carreras, Màrquez, et al. - 2004 |
