TY - GEN
T1 - Instance-based evaluation of entailment rule acquisition
AU - Szpektor, Idan
AU - Shnarch, Eyal
AU - Dagan, Ido
PY - 2007
Y1 - 2007
N2 - Obtaining large volumes of inference knowledge, such as entailment rules, has become a major factor in achieving robust semantic processing. While there has been substantial research on learning algorithms for such knowledge, their evaluation methodology has been problematic, hindering further research. We propose a novel evaluation methodology for entailment rules which explicitly addresses their semantic properties and yields satisfactory human agreement levels. The methodology is used to compare two state of the art learning algorithms, exposing critical issues for future progress.
AB - Obtaining large volumes of inference knowledge, such as entailment rules, has become a major factor in achieving robust semantic processing. While there has been substantial research on learning algorithms for such knowledge, their evaluation methodology has been problematic, hindering further research. We propose a novel evaluation methodology for entailment rules which explicitly addresses their semantic properties and yields satisfactory human agreement levels. The methodology is used to compare two state of the art learning algorithms, exposing critical issues for future progress.
UR - http://www.scopus.com/inward/record.url?scp=84860519734&partnerID=8YFLogxK
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AN - SCOPUS:84860519734
SN - 9781932432862
T3 - ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics
SP - 456
EP - 463
BT - ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics
T2 - 45th Annual Meeting of the Association for Computational Linguistics, ACL 2007
Y2 - 23 June 2007 through 30 June 2007
ER -