Automatic domain adaptation of word sense disambiguation based on sublanguage semantic schemata applied to clinical narrative

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Publication Type dissertation
School or College School of Medicine
Department Biomedical Informatics
Author Patterson, Olga
Title Automatic domain adaptation of word sense disambiguation based on sublanguage semantic schemata applied to clinical narrative
Date 2012-05
Description Domain adaptation of natural language processing systems is challenging because it requires human expertise. While manual e ort is e ective in creating a high quality knowledge base, it is expensive and time consuming. Clinical text adds another layer of complexity to the task due to privacy and con dentiality restrictions that hinder the ability to share training corpora among di erent research groups. Semantic ambiguity is a major barrier for e ective and accurate concept recognition by natural language processing systems. In my research I propose an automated domain adaptation method that utilizes sublanguage semantic schema for all-word word sense disambiguation of clinical narrative. According to the sublanguage theory developed by Zellig Harris, domain-speci c language is characterized by a relatively small set of semantic classes that combine into a small number of sentence types. Previous research relied on manual analysis to create language models that could be used for more e ective natural language processing. Building on previous semantic type disambiguation research, I propose a method of resolving semantic ambiguity utilizing automatically acquired semantic type disambiguation rules applied on clinical text ambiguously mapped to a standard set of concepts. This research aims to provide an automatic method to acquire Sublanguage Semantic Schema (S3) and apply this model to disambiguate terms that map to more than one concept with di erent semantic types. The research is conducted using unmodi ed MetaMap version 2009, a concept recognition system provided by the National Library of Medicine, applied on a large set of clinical text. The project includes creating and comparing models, which are based on unambiguous concept mappings found in seventeen clinical note types. The e ectiveness of the nal application was validated through a manual review of a subset of processed clinical notes using recall, precision and F-score metrics.
Type Text
Publisher University of Utah
Subject MESH Medical Informatics; Electronic Health Records; Health Insurance Portability and Accountability Act; Natural Language Processing; Unified Medical Language System; Sublanguage Semantic Schema; Word Sense Disambiguation Disambiguation
Dissertation Institution University of Utah
Dissertation Name Doctor of Philosophy
Language eng
Relation is Version of Digital reproduction of Automatic Domain Adaptation of Word Sense Disambigation Based on Sublanguage Semantic Schemata Applied to Clinical Narrative. Spencer S. Eccles Health Sciences Library. Print version available at J. Willard Marriott Library Special Collections.
Rights Management Copyright © Olga Patterson 2012
Format Medium application/pdf
Format Extent 1,159,278 bytes
Source Original in Marriott Library Special Collections.
ARK ark:/87278/s6fr34tp
Setname ir_etd
ID 196387
Reference URL https://collections.lib.utah.edu/ark:/87278/s6fr34tp