2019 •
Trained models for multi-task multi-dataset learning for text classification as well as sequence tagging in tweets
Authors:
Mishra, Shubhanshu
Abstract:
Trained models for multi-task multi-dataset learning for text classification as well as sequence tagging in tweets. Classification tasks include sentiment prediction, abusive content, sarcasm, and veridictality. Sequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging. Models were trained using: https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification_tagging.py See https://github.com/socialmediaie/SocialMediaIE and https://socialmediaie.github.io for details. If you (...)
Trained models for multi-task multi-dataset learning for text classification as well as sequence tagging in tweets. Classification tasks include sentiment prediction, abusive content, sarcasm, and veridictality. Sequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging. Models were trained using: https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification_tagging.py See https://github.com/socialmediaie/SocialMediaIE and https://socialmediaie.github.io for details. If you are using this data, please also cite the related article: Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929 (Read More)
Artificial intelligence |
Natural language processing |
Information retrieval |
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