{"type":"ScholarlyArticle","title":"Data-Efficient Algorithms and Neural Natural Language Processing: Applications in the Healthcare Domain","authors":[{"name":"Heereen Shim","slug":"heereen-shim","given":"Heereen","family":"Shim","affiliation":"Department of Electrical Engineering (ESAT) , STADIUS , KU Leuven , Leuven , Belgium"}],"abstract":"Recently proposed pre-trained language models can be easily fine-tuned to a wide range of downstream tasks. However, fine-tuning requires a large training set. This PhD project introduces novel natural language processing (NLP) use cases in the healthcare domain where obtaining a large training dataset is difficult and expensive. To this end, we propose data-efficient algorithms to fine-tune NLP models in low-resource settings and validate their effectiveness. We expect the outcomes of this PhD project could contribute to the NLP research and low-resource application domains.","doi":"10.24963/ijcai.2022/839","url":"https://ijcai.org/proceedings/2022/839","pdf":"https://ijcai.org/proceedings/2022/0839.pdf","markdown":"https://ijcai.org/proceedings/2022/839.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/839","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":839,"pages":{"first":5873,"last":5874},"keywords":["Machine Learning (ML): General","Speech & Natural Language Processing (SNLP): General"],"videos":[],"references":[{"position":1,"raw":"[Devlin et al., 2019] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understand- ing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, 2019.","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","year":"2019","venue":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Shim et al., 2020] Heereen Shim, Stijn Luca, Dietwig Lowet, and Bart Vanrumste. Data augmentation and semi- supervised learning for deep neural networks-based text classifier. In Proceedings of the 35th Annual ACM Sympo- sium on Applied Computing, pages 1119-1126, 2020.","title":"Data augmentation and semisupervised learning for deep neural networks-based text classifier","authors":"Shim","year":"2020","venue":"Proceedings of the 35th Annual ACM Symposium on Applied Computing","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Shim et al., 2021a] Heereen Shim, Dietwig Lowet, Stijn Luca, and Bart Vanrumste. Lets: A label-efficient train- ing scheme for aspect-based sentiment analysis by using a pre-trained language model. IEEE Access, 9:115563- 115578, 2021.","title":"Lets: A label-efficient training scheme for aspect-based sentiment analysis by using a pre-trained language model","authors":"Shim","year":"2021","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Shim et al., 2021b] Heereen Shim, Dietwig Lowet, Stijn Luca, and Bart Vanrumste. Synthetic data generation and multi-task learning for extracting temporal informa- tion from health-related narrative text. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W- NUT 2021), pages 260-273, Online, November 2021. As- sociation for Computational Linguistics.","title":"Synthetic data generation and multi-task learning for extracting temporal information from health-related narrative text","authors":"Shim","year":"2021","venue":"Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}