The Unified Medical Language System SPECIALIST Lexicon and Lexical Tools: Development and applications

Abstract

Natural language processing (NLP) plays a vital role in modern medical informatics. It converts narrative text or unstructured data into knowledge by analyzing and extracting concepts. A comprehensive lexical system is the foundation to the success of NLP applications and an essential component at the beginning of the NLP pipeline. The SPECIALIST Lexicon and Lexical Tools, distributed by the National Library of Medicine as one of the Unified Medical Language System Knowledge Sources, provides an underlying resource for many NLP applications. This article reports recent developments of 3 key components in the Lexicon. The core NLP operation of Unified Medical Language System concept mapping is used to illustrate the importance of these developments. Our objective is to provide generic, broad coverage and a robust lexical system for NLP applications. A novel multiword approach and other planned developments are proposed.

A deep database of medical abbreviations and acronyms for natural language processing

“…We performed long form normalization using the UMLS Lexical Variation Generation (UMLS-LVG; https://nlm.nih.gov/research/umls ) version 2019AB, an open-source toolset for transforming clinical text into a single canonical (i.e., normalized) form 50 . We modified the standard UMLS-LVG normalization flow options to avoid alphabetical sorting.…”

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

“…Chris et al summarized the latest development of three key components in the NLP library and proved their importance with examples. Their purpose is to provide an applicable and effective NLP vocabulary system [14]. Many scholars in various fields have been committed to promoting data mining for a long time.…”

“…Through analysis of the data over the past three years, they put forward some suggestions on attracting users in the COVID-19 crisis. At present, there are many methods that have been proposed for "network data mining", such as latent Dirichlet allocation (LDA) model [4,5], long short-term memory (LSTM) [6,7], Biterm method for short text distance measurement (BDM) [8,9], targeted aspects oriented topic modeling (TATM) [10], swarm intelligence (SI) algorithms [11], natural language processing (NLP) [12][13][14][15], etc., but they all have their advantages and disadvantages [16][17][18][19][20][21].…”

The UMLS knowledge sources at 30: indispensable to current research and applications in biomedical informatics

“…A case report by Lu et al describes recent developments in the SPECIALIST Lexicon and lexical tools that further enhance their utility for NLP research and applications. 22 Vasilakes et al report the results of an assessment of the coverage of dietary supplement terminology in the UMLS Metathesaurus and identify a method for improving it. 23…”