Illinois Data Bank - Dataset

Trained models for multi-task multi-dataset learning for sequence prediction in tweets
Cite this dataset:
Mishra, Shubhanshu (2019): Trained models for multi-task multi-dataset learning for sequence prediction in tweets. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0934773_V1
Dataset Description

Trained models for multi-task multi-dataset learning for sequence tagging in tweets.
Sequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging.

Models were trained using: https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.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
Subject Social Sciences
Keywords twitter; deep learning; machine learning; trained models; multi-task learning; multi-dataset learning;
License CC BY
Corresponding Creator Shubhanshu Mishra
Downloaded 2117 times
Version DOI Comment Publication Date
1 10.13012/B2IDB-0934773_V1 2019-09-17

Open in Globus File Manager what's this?
2.62 GB File
3.59 GB File
3.62 GB File
3.6 GB File
3.6 GB File
3.6 GB File
3.6 GB File
3.34 GB File
3.61 GB File
3.7 GB File
3.68 GB File
3.68 GB File
3.68 GB File
3.68 GB File
3.59 GB File
3.59 GB File
3.59 GB File
3.58 GB File
3.61 GB File
3.6 GB File
3.6 GB File
3.59 GB File
3.59 GB File
3.58 GB File
3.61 GB File
3.27 GB File
2.29 GB File
3.59 GB File
3.58 GB File
3.28 GB File
3.59 GB File
3.59 GB File
3.59 GB File
3.58 GB File
3.28 GB File
3.59 GB File
3.59 GB File
3.59 GB File
3.58 GB File
3.61 GB File
3.59 GB File
3.59 GB File
3.59 GB File
1.95 GB File
2.3 GB File
3.59 GB File
2.94 GB File
3.59 GB File
3.58 GB File
3.61 GB File
3.59 GB File
3.59 GB File
3.59 GB File
3.58 GB File
3.61 GB File
3.59 GB File
3.59 GB File
3.59 GB File
3.58 GB File
3.61 GB File
3.59 GB File
3.59 GB File
3.27 GB File
2.28 GB File
2.95 GB File
3.27 GB File
3.26 GB File
2.94 GB File
3.58 GB File
2.95 GB File
3.59 GB File
2.61 GB File
2.28 GB File
2.28 GB File
1.64 GB File
2.29 GB File
3.59 GB File
3.59 GB File
3.58 GB File
3.61 GB File
3.59 GB File

Contact the Research Data Service for help interpreting this log.

RelatedMaterial update: {"note"=>[nil, ""], "feature"=>[nil, false]} 2024-01-26T20:10:42Z
RelatedMaterial update: {"note"=>[nil, ""], "feature"=>[nil, false]} 2024-01-26T20:10:42Z
RelatedMaterial update: {"uri"=>["", "10.1145/3342220.3344929"], "uri_type"=>["", "DOI"], "note"=>[nil, ""], "feature"=>[nil, false]} 2024-01-26T20:10:42Z
RelatedMaterial update: {"uri"=>["", "https://github.com/socialmediaie/SocialMediaIE"], "uri_type"=>["", "URL"], "datacite_list"=>["IsSupplementTo", "IsSupplementedBy"], "note"=>[nil, ""], "feature"=>[nil, false]} 2024-01-26T20:10:42Z
RelatedMaterial create: {"material_type"=>"Presentation", "availability"=>nil, "link"=>"https://socialmediaie.github.io/tutorials/IC2S2_2020/Presentation.pdf", "uri"=>"https://socialmediaie.github.io/tutorials/IC2S2_2020/Presentation.pdf", "uri_type"=>"URL", "citation"=>"Mishra, S., Rezapour, R. & Diesner, J. (2020). Social Media Information Extraction Tutorials", "dataset_id"=>914, "selected_type"=>"Presentation", "datacite_list"=>"IsSupplementTo"} 2020-08-04T21:31:30Z
RelatedMaterial create: {"material_type"=>"Presentation", "availability"=>nil, "link"=>"https://socialmediaie.github.io/tutorials/HT2019/Tutorial-Slides-HT-Hof-Germany-17_09_2019.pdf", "uri"=>"https://socialmediaie.github.io/tutorials/HT2019/Tutorial-Slides-HT-Hof-Germany-17_09_2019.pdf", "uri_type"=>"URL", "citation"=>"Mishra, S., Diesner J., Hands on advanced machine learning for information extraction from tweets tasks, data, and open source tools. Sep, 2019", "dataset_id"=>914, "selected_type"=>"Presentation", "datacite_list"=>"IsSupplementTo"} 2019-11-04T16:26:24Z
RelatedMaterial update: {"datacite_list"=>["", "IsSupplementTo"]} 2019-11-04T16:26:24Z
RelatedMaterial update: {"datacite_list"=>["", "IsSupplementTo"]} 2019-11-04T16:26:24Z
RelatedMaterial update: {"uri"=>[nil, ""], "uri_type"=>[nil, ""], "datacite_list"=>[nil, ""]} 2019-09-17T15:43:42Z
RelatedMaterial update: {"uri"=>[nil, ""], "uri_type"=>[nil, ""], "datacite_list"=>[nil, ""]} 2019-09-17T15:43:42Z
Dataset update: {"description"=>["Trained models for multi-task multi-dataset learning for sequence tagging in tweets \r\nSequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging.\r\n\r\nModels were trained using: https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py\r\nSee https://github.com/socialmediaie/SocialMediaIE and https://socialmediaie.github.io for details.\r\n\r\nIf you are using this data pleace cite: \r\nShubhanshu 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", "Trained models for multi-task multi-dataset learning for sequence tagging in tweets.\r\nSequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging.\r\n\r\nModels were trained using: <a href=\"https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py\">https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py</a>\r\nSee <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.\r\n\r\nIf you are using this data, please also cite the related article: \r\nShubhanshu 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"], "version_comment"=>[nil, ""], "subject"=>[nil, "Social Sciences"]} 2019-09-17T15:43:42Z