2023

  1. Risk Adjustment for Regional Health Care Funding Allocations with Ensemble Methods: An Empirical Study and Interpretation.

    Tuukka Holster, Shaoxiong Ji, and Pekka Marttinen.

    The European Journal of Health Economics, 2023.

  2. Towards Interpretable Mental Health Analysis with Large Language Models.

    Kailai Yang, Shaoxiong Ji, Tianlin Zhang, Qianqian Xie, and Sophia Ananiadou.

    In Proceedings of EMNLP, 2023.  

  3. Weak Supervision and Clustering-Based Sample Selection for Clinical Named Entity Recognition.

    Wei Sun, Shaoxiong Ji, Tuulia Denti, Hans Moen, Oleg Kerro, Antti Rannikko, Pekka Marttinen, and Miika Koskinen.

    In ECML-PKDD, 2023.  

  4. A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge.

    Kailai Yang, Tianlin Zhang, Shaoxiong Ji, and Sophia Ananiadou.

    In Proceedings of CIKM, 2023.

  5. HPLT: High Performance Language Technologies.

    Mikko Aulamo, Nikolay Bogoychev, Shaoxiong Ji, Graeme Nail, Gema Ramírez-Sánchez, Jörg Tiedemann, Jelmer Van Der Linde, and Jaume Zaragoza.

    In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, 2023.  

  6. Ensemble Hybrid Learning Methods for Automated Depression Detection.

    Luna Ansari, Shaoxiong Ji, Qian Chen, and Erik Cambria.

    IEEE Transactions on Computational Social Systems, 2023.    

  7. Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning.

    Shaoxiong Ji, and Pekka Marttinen.

    In Proceedings of EACL, 2023.  

  8. Emotion fusion for mental illness detection from social media: A survey.

    Tianlin Zhang, Kailai Yang, Shaoxiong Ji, and Sophia Ananiadou.

    Information Fusion, 2023.  


2022

  1. Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing.

    Shaoxiong Ji

    In Findings of EMNLP, 2022.  

  2. Automated Clinical Coding: What, Why, and Where We Are?.

    Hang Dong, Matúš Falis, William Whiteley, Beatrice Alex, Joshua Matteson, Shaoxiong Ji, Jiaoyan Chen, and Honghan Wu.

    npj Digital Medicine, 2022.    

  3. Multitask Balanced and Recalibrated Network for Medical Code Prediction.

    Wei Sun, Shaoxiong Ji, Erik Cambria, and Pekka Marttinen.

    ACM Transactions on Intelligent Systems and Technology, 2022.      

  4. Contextualized Graph Embeddings for Adverse Drug Event Detection.

    Ya Gao, Shaoxiong Ji, Tongxuan Zhang, Prayag Tiwari, and Pekka Marttinen.

    In Proceedings of ECML-PKDD, 2022.      

  5. AaltoNLP at SemEval-2022 Task 11: Ensembling Task-adaptive Pretrained Transformers for Multilingual Complex NER.

    Aapo Pietiläinen, and Shaoxiong Ji.

    In Proceedings of International Workshop on Semantic Evaluation, 2022.      

  6. Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning.

    Shaoxiong Ji, Wenqi Jiang, Anwar Walid, and Xue Li.

    IEEE Intelligent Systems, 2022.      

  7. Natural Language Processing Applied to Mental Illness Detection: A Narrative Review.

    Tianlin Zhang, Annika Schoene, Shaoxiong Ji, and Sophia Ananiadou.

    npj Digital Medicine, 2022.  

  8. MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare.

    Shaoxiong Ji, Tianlin Zhang, Luna Ansari, Jie Fu, Prayag Tiwari, and Erik Cambria.

    In Proceedings of LREC, 2022.      

  9. A Survey on Knowledge Graphs: Representation, Acquisition and Applications.

    Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and Philip S Yu.

    IEEE Transactions on Neural Networks and Learning Systems, 2022.        

  10. Graph-powered Machine Learning in Future-generation Computing Systems.

    Shirui Pan, Shaoxiong Ji, Di Jin, Feng Xia, and Philip S. Yu.

    Future Generation Computer Systems, 2022.    

  11. BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis.

    Wei Li, Wei Shao, Shaoxiong Ji, and Erik Cambria.

    Neurocomputing, 2022.        

  12. Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks.

    Shaoxiong Ji, Xue Li, Zi Huang, and Erik Cambria.

    Neural Computing and Applications, 2022.      


2021

  1. Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning.

    Shaoxiong Ji, and Pekka Marttinen.

    arXiv preprint arXiv:2109.03062, 2021.    

  2. Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning.

    Shaoxiong Ji, Teemu Saravirta, Shirui Pan, Guodong Long, and Anwar Walid.

    arXiv preprint arXiv:2102.12920, 2021.      

  3. Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study.

    Shaoxiong Ji, Matti Hölttä, and Pekka Marttinen.

    Computers in Biology and Medicine, 2021.        

  4. Knowledge Graph Representation and Reasoning.

    Erik Cambria, Shaoxiong Ji, Shirui Pan, and Philip S. Yu.

    Neurocomputing, 2021.    

  5. Sequential Fusion of Facial Appearance and Dynamics for Depression Recognition.

    Qian Chen, Iti Chaturvedi, Shaoxiong Ji, and Erik Cambria.

    Pattern Recognition Letters, 2021.    

  6. Multitask Recalibrated Aggregation Network for Medical Code Prediction.

    Wei Sun, Shaoxiong Ji, Erik Cambria, and Pekka Marttinen.

    In Proceedings of ECML-PKDD, 2021.        

  7. Medical Code Assignment with Gated Convolution and Note-Code Interaction.

    Shaoxiong Ji, Shirui Pan, and Pekka Marttinen.

    In Findings of ACL-IJCNLP, 2021.        

  8. Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications.

    Shaoxiong Ji, Shirui Pan, Xue Li, Erik Cambria, Guodong Long, and Zi Huang.

    IEEE Transactions on Computational Social Systems, 2021.      


2020

  1. Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text.

    Shaoxiong Ji, Erik Cambria, and Pekka Marttinen.

    In Proceedings of the 3rd Clinical Natural Language Processing Workshop, 2020.        

  2. Time Series Indexing By Dynamic Covering with Cross-Range Constraints.

    Tao Sun, Hongbo Liu, Seán McLoone, Shaoxiong Ji, and Xindong Wu.

    The VLDB Journal, 2020.          

  3. Decentralized Knowledge Acquisition for Mobile Internet Applications.

    Jing Jiang, Shaoxiong Ji, and Guodong Long.

    World Wide Web, 2020.  

  4. Suicidal Ideation Detection in Online Social Content.

    Shaoxiong Ji

    Master of Philosophy, The University of Queensland, Australia, 2020.    


2019

  1. Learning Private Neural Language Modeling with Attentive Aggregation.

    Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, and Zi Huang.

    In Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), 2019.            

  2. Detecting Suicidal Ideation with Data Protection in Online Communities.

    Shaoxiong Ji, Guodong Long, Shirui Pan, Tianqing Zhu, Jing Jiang, and Sen Wang.

    In International Conference on Database Systems for Advanced Applications (DASFAA), 2019.  


2018

  1. Supervised Learning for Suicidal Ideation Detection in Online User Content.

    Shaoxiong Ji, Celina Ping Yu, Sai-fu Fung, Shirui Pan, and Guodong Long.

    Complexity, 2018.