Abstract
In this paper we present the use of a "general purpose" textual entailment recognizer in the Answer Validation Exercise (AVE) task. Our system is designed to learn entailment rules from annotated examples. Its main feature is the use of Support Vector Machines (SVMs) with kernel functions based on cross-pair similarity between entailment pairs. We experimented with our system using different training sets: RTE and AVE data sets. The comparative results show that entailment rules can be learned. Although, the high variability of the outcome prevents us to derive definitive conclusions, the results show that our approach is quite promising and improvable in the future.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 510-517 |
| Number of pages | 8 |
| Volume | 4730 LNCS |
| Publication status | Published - 1 Dec 2007 |
| Externally published | Yes |
| Event | 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006 - Alicante, Spain Duration: 20 Sep 2006 → 22 Sep 2006 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 4730 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006 |
|---|---|
| Country | Spain |
| City | Alicante |
| Period | 20/9/06 → 22/9/06 |
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ASJC Scopus subject areas
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science
Cite this
Experimenting a "general purpose" textual entailment learner in AVE. / Zanzotto, Fabio Massimo; Moschitti, Alessandro.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4730 LNCS 2007. p. 510-517 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4730 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Experimenting a "general purpose" textual entailment learner in AVE
AU - Zanzotto, Fabio Massimo
AU - Moschitti, Alessandro
PY - 2007/12/1
Y1 - 2007/12/1
N2 - In this paper we present the use of a "general purpose" textual entailment recognizer in the Answer Validation Exercise (AVE) task. Our system is designed to learn entailment rules from annotated examples. Its main feature is the use of Support Vector Machines (SVMs) with kernel functions based on cross-pair similarity between entailment pairs. We experimented with our system using different training sets: RTE and AVE data sets. The comparative results show that entailment rules can be learned. Although, the high variability of the outcome prevents us to derive definitive conclusions, the results show that our approach is quite promising and improvable in the future.
AB - In this paper we present the use of a "general purpose" textual entailment recognizer in the Answer Validation Exercise (AVE) task. Our system is designed to learn entailment rules from annotated examples. Its main feature is the use of Support Vector Machines (SVMs) with kernel functions based on cross-pair similarity between entailment pairs. We experimented with our system using different training sets: RTE and AVE data sets. The comparative results show that entailment rules can be learned. Although, the high variability of the outcome prevents us to derive definitive conclusions, the results show that our approach is quite promising and improvable in the future.
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M3 - Conference contribution
SN - 9783540749981
VL - 4730 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 510
EP - 517
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -