The use of complex grammatical features in statistical language learning assumes the availability of large scale training data and good quality parsers, especially for language different from English. In this paper, we show how good quality FrameNet SRL systems can be obtained, without relying on full syntactic parsing, by backing off to surface grammatical representations and structured learning. This model is here shown to achieve state-of-art results in standard benchmarks, while its robustness is confirmed in poor training conditions, for a language different for English, i.e. Italian. © 2011 Springer-Verlag Berlin Heidelberg.
Croce, D., Basili, R. (2011). Structured learning for semantic role labeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.238-249) [10.1007/978-3-642-23954-0_23].
Structured learning for semantic role labeling
CROCE, DANILO;BASILI, ROBERTO
2011-09-01
Abstract
The use of complex grammatical features in statistical language learning assumes the availability of large scale training data and good quality parsers, especially for language different from English. In this paper, we show how good quality FrameNet SRL systems can be obtained, without relying on full syntactic parsing, by backing off to surface grammatical representations and structured learning. This model is here shown to achieve state-of-art results in standard benchmarks, while its robustness is confirmed in poor training conditions, for a language different for English, i.e. Italian. © 2011 Springer-Verlag Berlin Heidelberg.File | Dimensione | Formato | |
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