TY - GEN
T1 - Unsupervised entity linking with abstract meaning representation
AU - Pan, Xiaoman
AU - Cassidy, Taylor
AU - Hermjakob, Ulf
AU - Ji, Heng
AU - Knight, Kevin
N1 - Publisher Copyright:
© 2015 Association for Computational Linguistics.
PY - 2015
Y1 - 2015
N2 - Most successful Entity Linking (EL) methods aim to link mentions to their referent entities in a structured Knowledge Base (KB) by comparing their respective contexts, often using similarity measures. While the KB structure is given, current methods have suffered from impoverished information representations on the mention side. In this paper, we demonstrate the effectiveness of Abstract Meaning Representation (AMR) (Banarescu et al., 2013) to select high quality sets of entity "collaborators" to feed a simple similarity measure (Jaccard) to link entity mentions. Experimental results show that AMR captures contextual properties discriminative enough to make linking decisions, without the need for EL training data, and that system with AMR parsing output outperforms hand labeled traditional semantic roles as context representation for EL. Finally, we show promising preliminary results for using AMR to select sets of "coherent" entity mentions for collective entity linking.
AB - Most successful Entity Linking (EL) methods aim to link mentions to their referent entities in a structured Knowledge Base (KB) by comparing their respective contexts, often using similarity measures. While the KB structure is given, current methods have suffered from impoverished information representations on the mention side. In this paper, we demonstrate the effectiveness of Abstract Meaning Representation (AMR) (Banarescu et al., 2013) to select high quality sets of entity "collaborators" to feed a simple similarity measure (Jaccard) to link entity mentions. Experimental results show that AMR captures contextual properties discriminative enough to make linking decisions, without the need for EL training data, and that system with AMR parsing output outperforms hand labeled traditional semantic roles as context representation for EL. Finally, we show promising preliminary results for using AMR to select sets of "coherent" entity mentions for collective entity linking.
UR - http://www.scopus.com/inward/record.url?scp=84960158690&partnerID=8YFLogxK
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U2 - 10.3115/v1/n15-1119
DO - 10.3115/v1/n15-1119
M3 - Conference contribution
AN - SCOPUS:84960158690
T3 - NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
SP - 1130
EP - 1139
BT - NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015
Y2 - 31 May 2015 through 5 June 2015
ER -