Authors:
Chris J. Lu
1
;
Destinee Tormey
2
;
Lynn McCreedy
2
and
Allen C. Browne
2
Affiliations:
1
National Library of Medicine, Medical Science & Computing and LLC, United States
;
2
National Library of Medicine, United States
Keyword(s):
Derivations, Suffix Derivations, SD-Rules, Natural Language Processing, the SPECIALIST Lexical Tools.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Design and Development Methodologies for Healthcare IT
;
Enterprise Information Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Suffix derivations (SDs) are used with query expansion in concept mapping as an effective Natural Language Processing (NLP) technique to improve recall without sacrificing precision. A systematic approach was proposed to generate derivations in the SPECIALIST Lexical Tools in which SD candidate rules were used to retrieve SD-pairs from the SPECIALIST Lexicon (Lu et al., 2012). Good SD candidate rules are gathered as SD-Rules in Lexical Tools for generating SDs that are not known to the Lexicon. This paper describes a methodology to select an optimized SD-Rule set that meets our requirement of 95\% system precision with best system performance from SD candidate rules. The results of the latest three releases of Lexical Tools show: 1) system precision and recall of selected SD-Rules are above 95\%. 2) a consistency between a computational linguistic approach and traditional linguistic knowledge for selecting the best Parent-Child rules. 3) a consistent approach yielding similar SD-Rule
sets and system performance. Ultimately, it results in better precision and recall for NLP applications using Lexical Tools derivational related flow components.
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