Abstract
We present a simple, two-steps supervised strategy for the identification and classification of thematic roles in natural language texts. We employ no external source of information but automatic parse trees of the input sentences. We use a few attribute-value features and tree kernel functions applied to specialized structured features. Different configurations of our thematic role labeling system took part in 2 tasks of the SemEval 2007 evaluation campaign, namely the closed tasks on semantic role labeling for the English and the Arabic languages. In this paper we present and discuss the system configuration that participated in the English semantic role labeling task and present new results obtained after the end of the evaluation campaign.
Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Pages | 350-361 |
Number of pages | 12 |
Volume | 4733 LNAI |
Publication status | Published - 1 Dec 2007 |
Externally published | Yes |
Event | 10th Congress of the Italian Association for Artificial Intelligence, AI IA 2007 - Rome, Italy Duration: 10 Sep 2007 → 13 Sep 2007 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 4733 LNAI |
ISSN (Print) | 03029743 |
ISSN (Electronic) | 16113349 |
Other
Other | 10th Congress of the Italian Association for Artificial Intelligence, AI IA 2007 |
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Country | Italy |
City | Rome |
Period | 10/9/07 → 13/9/07 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science
Cite this
A tree kernel-based shallow semantic parser for thematic role extraction. / Pighin, Daniele; Moschitti, Alessandro.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4733 LNAI 2007. p. 350-361 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4733 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A tree kernel-based shallow semantic parser for thematic role extraction
AU - Pighin, Daniele
AU - Moschitti, Alessandro
PY - 2007/12/1
Y1 - 2007/12/1
N2 - We present a simple, two-steps supervised strategy for the identification and classification of thematic roles in natural language texts. We employ no external source of information but automatic parse trees of the input sentences. We use a few attribute-value features and tree kernel functions applied to specialized structured features. Different configurations of our thematic role labeling system took part in 2 tasks of the SemEval 2007 evaluation campaign, namely the closed tasks on semantic role labeling for the English and the Arabic languages. In this paper we present and discuss the system configuration that participated in the English semantic role labeling task and present new results obtained after the end of the evaluation campaign.
AB - We present a simple, two-steps supervised strategy for the identification and classification of thematic roles in natural language texts. We employ no external source of information but automatic parse trees of the input sentences. We use a few attribute-value features and tree kernel functions applied to specialized structured features. Different configurations of our thematic role labeling system took part in 2 tasks of the SemEval 2007 evaluation campaign, namely the closed tasks on semantic role labeling for the English and the Arabic languages. In this paper we present and discuss the system configuration that participated in the English semantic role labeling task and present new results obtained after the end of the evaluation campaign.
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M3 - Conference contribution
AN - SCOPUS:38049123637
SN - 9783540747819
VL - 4733 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 350
EP - 361
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -