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: <a href="https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification_tagging.py">https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification_tagging.py</a>
See <a href="https://github.com/socialmediaie/SocialMediaIE">https://github.com/socialmediaie/SocialMediaIE</a> and <a href="https://socialmediaie.github.io">https://socialmediaie.github.io</a> 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 Urbana-Champaign

Keywords

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

Cite this