Kathleen Hamilton


pdf bib
Predicting the Focus of Negation: Model and Error Analysis
Md Mosharaf Hossain | Kathleen Hamilton | Alexis Palmer | Eduardo Blanco
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network. Experimental results show that doing so obtains the best results to date. Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.

pdf bib
Extracting Adherence Information from Electronic Health Records
Jordan Sanders | Meghana Gudala | Kathleen Hamilton | Nishtha Prasad | Jordan Stovall | Eduardo Blanco | Jane E Hamilton | Kirk Roberts
Proceedings of the 28th International Conference on Computational Linguistics

Patient adherence is a critical factor in health outcomes. We present a framework to extract adherence information from electronic health records, including both sentence-level information indicating general adherence information (full, partial, none, etc.) and span-level information providing additional information such as adherence type (medication or nonmedication), reasons and outcomes. We annotate and make publicly available a new corpus of 3,000 de-identified sentences, and discuss the language physicians use to document adherence information. We also explore models based on state-of-the-art transformers to automate both tasks.