Papers by James Thomas

3 papers
A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records (2025.acl-industry)

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Challenge: EHRs contain vast amounts of valuable clinical data, stored as unstructured text.
Approach: They propose a method that uses existing NER+L methods to classify medical entities at scale using a named entity recognition and linking task.
Outcome: The proposed model outperforms Bi-LSTM in minority class tasks with up to 28% of the time and 32% faster training time.
RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question (2023.findings-acl)

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Challenge: Existing metrics for evaluating the quality of automatically generated questions are expensive and penalise valid questions that may not have high lexical or semantic similarity to the reference questions.
Approach: They propose a question-answering and span scorer metric based on the answerability of the candidate question given the context.
Outcome: The proposed metric has higher correlation with human judgment without relying on the reference question.
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding (D18-1)

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Challenge: Existing methods for tagging unstructured texts with arbitrary number of terms drawn from an ontology are lacking.
Approach: They propose a model for tagging unstructured texts with an arbitrary number of terms drawn from an ontology.
Outcome: The proposed model yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts.

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