Rosa L. Figueroa BSc, Qing Zeng-Treitler PhD
We developed a method to help tailor a comprehensive vocabulary system (e.g. the UMLS) for a sub-domain (e.g. clinical reports) in support of natural language processing (NLP). The method detects unused senses in a sub-domain by comparing the relational neighborhood of a word-term in the vocabulary with the semantic neighborhood of the word-term in the sub-domain. The semantic neighborhood of the word-term in the sub-domain is determined using latent semantic analysis (LSA). We trained and tested the unused sense detection on two clinical text corpora: one contains discharge summaries and the other outpatient visit notes. We were able to detect unused senses with precision from 79% to 87%, recall from 48% to 74%, and an area under receiver operating curve (AUC) of 72% to 87%.
@article{29a1646b-2801-4ad1-8db3-20338cf871f3,
title={Tailoring vocabularies for NLP in sub do},
author={Rosa L. Figueroa BSc and Qing Zeng-Treitler PhD},
year={2026},
language={en}
}TY - JOUR TI - Tailoring vocabularies for NLP in sub do AU - Rosa L. Figueroa BSc AU - Qing Zeng-Treitler PhD PY - 2026 LA - en ER -
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