TY - GEN
T1 - Context-Aware entity morph decoding
AU - Zhang, Boliang
AU - Huang, Hongzhao
AU - Pan, Xiaoman
AU - Li, Sujian
AU - Lin, Chin Yew
AU - Ji, Heng
AU - Knight, Kevin
AU - Wen, Zhen
AU - Sun, Yizhou
AU - Han, Jiawei
AU - Yener, Bulent
N1 - Publisher Copyright:
© 2015 Association for Computational Linguistics.
PY - 2015
Y1 - 2015
N2 - People create morphs, a special type of fake alternative names, to achieve certain communication goals such as expressing strong sentiment or evading censors. For example, "Black Mamba", the name for a highly venomous snake, is a morph that Kobe Bryant created for himself due to his agility and aggressiveness in playing basketball games. This paper presents the first end-to-end context-Aware entity morph decoding system that can automatically identify, disambiguate, verify morph mentions based on specific contexts, and resolve them to target entities. Our approach is based on an absolute "cold-start"-it does not require any candidate morph or target entity lists as input, nor any manually constructed morph-target pairs for training. We design a semi-supervised collective inference framework for morph mention extraction, and compare various deep learning based approaches for morph resolution. Our approach achieved significant improvement over the state-of-The-Art method (Huang et al., 2013), which used a large amount of training data.
AB - People create morphs, a special type of fake alternative names, to achieve certain communication goals such as expressing strong sentiment or evading censors. For example, "Black Mamba", the name for a highly venomous snake, is a morph that Kobe Bryant created for himself due to his agility and aggressiveness in playing basketball games. This paper presents the first end-to-end context-Aware entity morph decoding system that can automatically identify, disambiguate, verify morph mentions based on specific contexts, and resolve them to target entities. Our approach is based on an absolute "cold-start"-it does not require any candidate morph or target entity lists as input, nor any manually constructed morph-target pairs for training. We design a semi-supervised collective inference framework for morph mention extraction, and compare various deep learning based approaches for morph resolution. Our approach achieved significant improvement over the state-of-The-Art method (Huang et al., 2013), which used a large amount of training data.
UR - http://www.scopus.com/inward/record.url?scp=84943739572&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84943739572&partnerID=8YFLogxK
U2 - 10.3115/v1/p15-1057
DO - 10.3115/v1/p15-1057
M3 - Conference contribution
AN - SCOPUS:84943739572
T3 - ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference
SP - 586
EP - 595
BT - ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference
PB - Association for Computational Linguistics (ACL)
T2 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015
Y2 - 26 July 2015 through 31 July 2015
ER -