Automatic labeling of semantic roles
Computational Linguistics
The Journal of Machine Learning Research
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Making computers laugh: investigations in automatic humor recognition
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches
ECML '07 Proceedings of the 18th European conference on Machine Learning
Domain adaptation of natural language processing systems
Domain adaptation of natural language processing systems
Design challenges and misconceptions in named entity recognition
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Semantic role labeling as sequential tagging
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Semantic role labeling for news tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Let's Gang Up on Cyberbullying
Computer
A Normative Agent System to Prevent Cyberbullying
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Named entity recognition in tweets: an experimental study
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Fast learning for sentiment analysis on bullying
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
More for your money: exploiting performance heterogeneity in public clouds
Proceedings of the Third ACM Symposium on Cloud Computing
Adaptive game for reducing aggressive behavior
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Detecting cyberbullying: query terms and techniques
Proceedings of the 5th Annual ACM Web Science Conference
| Hi-index | 0.00 |
We introduce the social study of bullying to the NLP community. Bullying, in both physical and cyber worlds (the latter known as cyberbullying), has been recognized as a serious national health issue among adolescents. However, previous social studies of bullying are handicapped by data scarcity, while the few computational studies narrowly restrict themselves to cyberbullying which accounts for only a small fraction of all bullying episodes. Our main contribution is to present evidence that social media, with appropriate natural language processing techniques, can be a valuable and abundant data source for the study of bullying in both worlds. We identify several key problems in using such data sources and formulate them as NLP tasks, including text classification, role labeling, sentiment analysis, and topic modeling. Since this is an introductory paper, we present baseline results on these tasks using off-the-shelf NLP solutions, and encourage the NLP community to contribute better models in the future.