Jie Gao

Orcid: 0000-0002-3610-8748

Affiliations:
  • Logically AI, London, UK
  • University of Sheffield, Department of Computer Science, UK (PhD 2020)


According to our database1, Jie Gao authored at least 11 papers between 2015 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2021
Logically at the Factify 2022: Multimodal Fact Verification.
CoRR, 2021

2020
RP-DNN: A Tweet Level Propagation Context Based Deep Neural Networks for Early Rumor Detection in Social Media.
Proceedings of The 12th Language Resources and Evaluation Conference, 2020

2019
Active 10: Brisk Walking to Support Regular Physical Activity.
Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2019

Neural language model based training data augmentation for weakly supervised early rumor detection.
Proceedings of the ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining, 2019

2018
SemRe-Rank: Improving Automatic Term Extraction by Incorporating Semantic Relatedness with Personalised PageRank.
ACM Trans. Knowl. Discov. Data, 2018

Mapping Mobility to Support Crisis Management.
Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management, 2018

2017
SemRe-Rank: Incorporating Semantic Relatedness to Improve Automatic Term Extraction Using Personalized PageRank.
CoRR, 2017

Supervised learning for robust term extraction.
Proceedings of the 2017 International Conference on Asian Language Processing, 2017

2016
JATE 2.0: Java Automatic Term Extraction with Apache Solr.
Proceedings of the Tenth International Conference on Language Resources and Evaluation LREC 2016, 2016

2015
The LODIE team (University of Sheffield) Participation at the TAC2015 Entity Discovery Task of the Cold Start KBP Track.
Proceedings of the 2015 Text Analysis Conference, 2015

Exploiting Linked Open Data to Uncover Entity Types.
Proceedings of the Semantic Web Evaluation Challenges, 2015


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