Trained models for multi-task multi-dataset learning for text classification as well as sequence tagging in tweets

  • Shubhanshu Mishra (Creator)

Dataset

Description

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
Date made availableSep 17 2019
PublisherUniversity of Illinois at Urbana-Champaign

Keywords

  • twitter
  • deep learning
  • machine learning
  • trained models
  • multi-task learning
  • multi-dataset learning
  • classification
  • sequence tagging

Cite this

Mishra, S. (Creator) (Sep 17 2019). Trained models for multi-task multi-dataset learning for text classification as well as sequence tagging in tweets . University of Illinois at Urbana-Champaign. 10.13012/B2IDB-1094364_V1