Context-aware Entity Morph Decoding

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages 586–595, Beijing, China, July 26-31, 2015. c⃝2015 Association for Computational Linguistics Context-aware Entity Morph Decoding Boliang Zhang1, Hongzhao Huang1, Xiaoman Pan1, Sujian Li2, Chin-Yew Lin3 Heng Ji1, Kevin Knight4, Zhen Wen5, Yizhou Sun6, Jiawei Han7, Bulent Yener1 1Rensselaer Polytechnic Institute, 2Peking University, 3Microsoft Research Asia, 4University of Southern California 5IBM T. J. Watson Research Center, 6Northeastern University, 7Univerisity of Illinois at Urbana-Champaign 1{zhangb8,huangh9,panx2,jih,yener}@rpi.edu, 2lisujian@pku.edu.cn, 3cyl@microsoft.com 4hanj@illinois.edu, 5zhenwen@us.ibm.com, 6yzsun@ccs.neu.edu, 7hanj@illinois.edu Abstract 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 bas- ketball games. This paper presents the first end-to-end context-aware entity morph de- coding system that can automatically iden- tify, 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 tar- get entity lists as input, nor any manually constructed morph-target pairs for train- ing. We design a semi-supervised collec- tive inference framework for morph men- tion extraction, and compare various deep learning based approaches for morph res- olution. Our approach achieved signifi- cant improvement over the state-of-the-art method (Huang et al., 2013), which used a large amount of training data. 1 1 Introduction Morphs (Huang et al., 2013; Zhang et al., 2014) refer to the fake alternative names created by so- cial media users to entertain readers or evade cen- sors. For example, during the World Cup in 2014, 1The data set and programs are publicly avail- able at: http://nlp.cs.rpi.edu/data/morphdecoding.zip and http://nlp.cs.rpi.edu/software/morphdecoding.tar.gz a morph “Su-tooth” was created to refer to the Uruguay striker “Luis Suarez” for his habit of bit- ing other players. Automatically decoding human- generated morphs in text is critical for downstream deep language understanding tasks such as entity linking and event argument extraction. However, even for human, it is difficult to de- code many morphs without certain historical, cul- tural, or political background knowledge (Zhang et al., 2014). For example, “The Hutt” can be used to refer to a fictional alien entity in the Star Wars universe (“The Hutt stayed and established himself as ruler of Nam Chorios”), or the governor of New Jersey, Chris Christie (“The Hutt announced a bid for a seat in the New Jersey General Assembly”). Huang et al. (2013) did a pioneering pilot study on morph resolution, but their approach assumed the entity morphs were already extracted and used a large amount of labeled data. In fact, they resolved morphs on corpus-level instead of mention-level and thus their approach was context-independent. A practical morph decoder, as depicted in Fig- ure 1, consists of two problems: (1) Morph Ex- traction: given a corpus, extract morph mentions; and …

Content truncated to 500 words