Naseem, Usman, Khushi, Matloob, Kim, Jinman, and Dunn, Adam G. (2023) Hybrid Text Representation for Explainable Suicide Risk Identification on Social Media. IEEE Transactions on Computational Social Systems. (In Press)

Naseem, Usman, Khushi, Matloob, Dunn, Adam G., and Kim, Jinman (2023) K-PathVQA: Knowledge-Aware Multimodal Representation for Pathology Visual Question Answering. IEEE Journal of Biomedical and Health Informatics. (In Press)

Rashid, Junaid, Kim, Jungeun, and Naseem, Usman (2023) Coherent Topic Modeling for Creative Multimodal Data on Social Media. In: Proceedings of the ACM Web Conference 2023. pp. 3923-3927. From: WWW 2023: The ACM Web Conference 2023, April 30 - May 4 2023, Austin, TX, USA.

Naseem, Usman, Kim, Jinman, Khushi, Matloob, and Dunn, Adam G. (2023) Graph-Based Hierarchical Attention Network for Suicide Risk Detection on Social Media. In: Proceedings of the ACM Web Conference 2023. pp. 995-1003. From: WWW 2023: The ACM Web Conference 2023: Companion of The World Wide Web Conference, April 30 - May 4 2023, Austin, TX, USA.

Ji, Junhui, Ren, Wei, and Naseem, Usman (2023) Identifying Creative Harmful Memes via Prompt based Approach. In: Proceedings of the ACM Web Conference 2023. pp. 3868-3872. From: WWW '23: The ACM Web Conference 2023, April 30 - May 4 2023, Austin, TX, USA.

Rashid, Junaid, Kim, Jungeun, and Naseem, Usman (2023) Incorporating Embedding to Topic Modeling for More Effective Short Text Analysis. In: Proceedings of the ACM Web Conference 2023. pp. 73-76. From: WWW 2023: The ACM Web Conference 2023: Companion of The World Wide Web Conference, April 30 - May 4 2023, Austin, TX, USA.

Naseem, Usman, Kim, Jinman, Khushi, Matloob, and Dunn, Adam G. (2023) A Multimodal Framework for the Identification of Vaccine Critical Memes on Twitter. In: Proceedings of the 16th ACM International Conference on Web Search and Data Mining. pp. 706-714. From: WSDM '23: 16th ACM International Conference on Web Search and Data Mining, 27 February - 3 March 2023, Singapore.

Ye, Tangwei, Hu, Liang, Zhang, Qi, Lai, Zhong Yuan, Naseem, Usman, and Liu, Dora D. (2023) Show Me The Best Outfit for A Certain Scene: A Scene-aware Fashion Recommender System. In: Proceedings of the ACM Web Conference 2023. pp. 1172-1180. From: WWW '23: The ACM Web Conference 2023, April 30 - May 4 2023, Austin, TX, USA.

Naseem, Usman, Khushi, Matloob, Kim, Jinman, and Dunn, Adam G. (2022) RHMD: A Real-World Dataset for Health Mention Classification on Reddit. IEEE Transactions on Computational Social Systems. (In Press)

Naseem, Usman, Lee, Byoung Chan, Khushi, Matloob, Kim, Jinman, and Dunn, Adam G. (2022) Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model. In: Proceedings of First Workshop on Efficient Benchmarking in NLP. pp. 22-31. From: NLP Power 2022: First Workshop on Efficient Benchmarking in NLP, 26 May 2022, Dublin, IRL.

Naseem, Usman, Dunn, Adam G., Khushi, Matloob, and Kim, Jinman (2022) Benchmarking for biomedical natural language processing tasks with a domain specific ALBERT. BMC Bioinformatics, 23. 144.

Naseem, Usman, Dunn, Adam G., Kim, Jinman, and Khushi, Matloob (2022) Early Identification of Depression Severity Levels on Reddit Using Ordinal Classification. In: Proceedings of the ACM Web Conference 2022. pp. 2563-2572. From: WWW '22: the ACM Web Conference 2022, 25-29 April 2022, Lyon, France.

Naseem, Usman, Kim, Jinman, Khushi, Matloob, and Dunn, Adam G. (2022) Identification of Disease or Symptom terms in Reddit to Improve Health Mention Classification. In: Proceedings of the ACM Web Conference 2022. pp. 2573-2581. From: WWW '22: the ACM Web Conference 2022, 25-29 April 2022, Lyon, France.

Naseem, Usman, Bandi, Ajay, Raza, Shaina, Rashid, Junaid, and Chakravarthi, Bharathi Raja (2022) Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation. In: Proceedings of the Proceedings of the 21st Workshop on Biomedical Language Processing. pp. 110-115. From: BioNLP 22: 21st Workshop on Biomedical Language Processing, 26 May 2022, Dublin, IRL.

Thapa, Surendrabikram, Shah, Aditya, Jafri, Farhan Ahmad, Naseem, Usman, and Razzak, Imran (2022) A Multi-Modal Dataset for Hate Speech Detection on Social Media: Case-study of Russia-Ukraine Conflict. In: Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text. From: CASE 2022: the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, 7-8 December 2022, Abu Dhabi, United Arab Emirates.

Naseem, Usman, Kim, Jinman, Khushi, Matloob, and Dunn, Adam G. (2022) Robust Identification of Figurative Language in Personal Health Mentions on Twitter. IEEE Transactions on Artificial Intelligence, 4 (2). pp. 362-372.

Naseem, Usman, Khushi, Matloob, and Kim, Jinman (2022) Vision-Language Transformer for Interpretable Pathology Visual Question Answering. IEEE Journal of Biomedical and Health Informatics, 27 (4). pp. 1681-1690.

Naseem, Usman, Khushi, Matloob, Reddy, Vinay, Rajendran, Sakthivel, Razzak, Imran, and Kim, Jinman (2021) BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition. In: Proceedings of the 2021 International Joint Conference on Neural Networks. From: IJCNN: 2021 International Joint Conference on Neural Networks, 18-22 July 2021, Shenzhen, China.

Naseem, Usman, Razzak, Imran, Khushi, Matloob, Eklund, Peter W., and Kim, Jinman (2021) COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis. IEEE Transactions on Computational Social Systems, 8 (4). pp. 1003-1015.

Naseem, Usman, Khushi, Matloob, Kim, Jinman, and Dunn, Adam (2021) Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU. In: Proceedings of the 2021 International Joint Conference on Neural Networks. From: IJCNN: 2021 International Joint Conference on Neural Networks, 18-22 July 2021, Shenzhen, China.

Naseem, Usman, Razzak, Imran, Khan, Shah Khalid, and Prasad, Mukesh (2021) A Comprehensive Survey on Word Representation Models: From Classical to State-of-the-Art Word Representation Language Models. ACM Transactions on Asian and Low-Resource Language Information Processing, 20 (5). 74.

Naseem, Usman, Musial, Katarzyna, Eklund, Peter, and Prasad, Mukesh (2020) Biomedical Named-Entity Recognition by Hierarchically Fusing BioBERT Representations and Deep Contextual-Level Word-Embedding. In: Proceedings of the 2020 International Joint Conference on Neural Networks. From: IJCNN: 2020 International Joint Conference on Neural Networks, 19-24 July 2020, Glasgow, UK.

Thapa, Surendrabikram, Adhikari, Surabhi, Naseem, Usman, Singh, Priyanka, Bharathy, Gnana, and Prasad, Mukesh (2020) Detecting Alzheimer's Disease by Exploiting Linguistic Information from Nepali Transcript. In: Communications in Computer and Information Science (1332) pp. 176-184. From: ICONIP 202: 27th International Conference on Neural Information Processing, 18-22 November 2020, Bangkok, Thailand.

Naseem, Usman, Razzak, Imran, Eklund, Peter, and Musial, Katarzyna (2020) Towards Improved Deep Contextual Embedding for the identification of Irony and Sarcasm. In: Proceedings of the 2020 International Joint Conference on Neural Networks. From: IJCNN: 2020 International Joint Conference on Neural Networks, 19-24 July 2020, Glasgow, UK.

Naseem, Usman, Razzak, Imran, Musial, Katarzyna, and Imran, Muhammad (2020) Transformer based Deep Intelligent Contextual Embedding for Twitter sentiment analysis. Future Generation Computer Systems, 113. pp. 58-69.

Naseem, Usman, Razzak, Imran, and Eklund, Peter W. (2020) A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on twitter. Multimedia Tools and Applications, 80. pp. 35239-35266.

Naseem, Usman, and Musial, Katarzyna (2019) DICE: Deep Intelligent Contextual Embedding for Twitter Sentiment Analysis. In: Proceedings of the 15th IAPR International Conference on Document Analysis and Recognition. pp. 953-958. From: ICDAR 2019: 15th IAPR International Conference on Document Analysis and Recognition, 20-25 September 2019, Sydney, NSW, Australia.