Papers by Jocelyn Dunstan
Simple Yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition (2022.coling-1)
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| Challenge: | Named Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories. |
| Approach: | They propose to revisit the Multiple LSTM-CRF (MLC) model, a simple, overlooked, yet powerful approach based on training independent sequence labeling models for each entity type. |
| Outcome: | The proposed model achieves state-of-the-art results in the Chilean Waiting List corpus by including pre-trained language models. |
Development of pre-trained language models for clinical NLP in Spanish (2023.eacl-srw)
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| Challenge: | Clinical natural language processing aims to tackle language and prediction tasks using text from medical practice, such as clinical notes, prescriptions, and discharge summaries. |
| Approach: | They propose to build a clinical corpus big enough to implement a functional PLM. |
| Outcome: | The proposed model will be able to handle language and prediction tasks using clinical text while using biomedical and general text. |