Chris J. Lu

A Systematic Approach for Medical Language Processing: Generating Derivational Variants

Medical language processing seeks to analyze linguistic patterns in electronic medical records, which requires managing lexical variations. A systematic approach to generating derivational variants, including prefixes, suffixes, and zero derivations, has improved precision and recall rates.

Generating a Distilled N-Gram Set - Effective Lexical Multiword Building in the SPECIALIST Lexicon.

Multiwords are vital to better Natural Language Processing (NLP) systems for more effective and efficient parsers, refining information retrieval searches, enhancing precision and recall in Medical Language Processing (MLP) applications, etc. The Lexical Systems Group has enhanced the coverage of multiwords in the Lexicon to provide a more comprehensive resource for such applications. This paper describes a new systematic approach to lexical multiword acquisition from MEDLINE through filters and matchers based on empirical models. The design goal, function description, various tests and applications of filters, matchers, and data are discussed. Results include: 1) Generating a smaller (38%) distilled MEDLINE n-gram set with better precision and similar recall to the MEDLINE n-gram set; 2) Establishing a system for generating high precision multiword candidates for effective Lexicon building. We believe the MLP/NLP community can benefit from access to these big data (MEDLINE n-gram) sets. We also anticipate an accelerated growth of multiwords in the Lexicon with this system. Ultimately, improvement in recall or precision can be anticipated in NLP projects using the MEDLINE distilled n-gram set, SPECIALIST Lexicon and its applications.

Generating SD-Rules in the SPECIALIST Lexical Tools

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.