Yan Wang1, Serguei Pakhomov2, James O Ryan1, Genevieve B Melton3. 1. Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States. 2. Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States; College of Pharmacy, University of Minnesota, Minneapolis, MN, United States. 3. Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States; Department of Surgery, University of Minnesota, Minneapolis, MN, United States. Electronic address: gmelton@umn.edu.
Abstract
BACKGROUND: Full syntactic parsing of clinical text as a part of clinical natural language processing (NLP) is critical for a wide range of applications. Several robust syntactic parsers are publicly available to produce linguistic representations for sentences. However, these existing parsers are mostly trained on general English text and may require adaptation for optimal performance on clinical text. Our objective was to adapt an existing general English parser for the clinical text of operative reports via lexicon augmentation, statistics adjusting, and grammar rules modification based on operative reports. METHOD: The Stanford unlexicalized probabilistic context-free grammar (PCFG) parser lexicon was expanded with SPECIALIST lexicon along with statistics collected from a limited set of operative notes tagged by two POS taggers (GENIA tagger and MedPost). The most frequently occurring verb entries of the SPECIALIST lexicon were adjusted based on manual review of verb usage in operative notes. Stanford parser grammar production rules were also modified based on linguistic features of operative reports. An analogous approach was then applied to the GENIA corpus to test the generalizability of this approach to biologic text. RESULTS: The new unlexicalized PCFG parser extended with the extra lexicon from SPECIALIST along with accurate statistics collected from an operative note corpus tagged with GENIA POS tagger improved the F-score by 2.26% from 87.64% to 89.90%. There was a progressive improvement with the addition of multiple approaches. Lexicon augmentation combined with statistics from the operative notes corpus provided the greatest improvement of parser performance. Application of this approach on the GENIA corpus increased the F-score by 3.81% with a simple new grammar and addition of the GENIA corpus lexicon. CONCLUSION: Using statistics collected from clinical text tagged with POS taggers along with proper modification of grammars and lexicons of an unlexicalized PCFG parser may improve parsing performance of existing parsers on specialized clinical text.
BACKGROUND: Full syntactic parsing of clinical text as a part of clinical natural language processing (NLP) is critical for a wide range of applications. Several robust syntactic parsers are publicly available to produce linguistic representations for sentences. However, these existing parsers are mostly trained on general English text and may require adaptation for optimal performance on clinical text. Our objective was to adapt an existing general English parser for the clinical text of operative reports via lexicon augmentation, statistics adjusting, and grammar rules modification based on operative reports. METHOD: The Stanford unlexicalized probabilistic context-free grammar (PCFG) parser lexicon was expanded with SPECIALIST lexicon along with statistics collected from a limited set of operative notes tagged by two POS taggers (GENIA tagger and MedPost). The most frequently occurring verb entries of the SPECIALIST lexicon were adjusted based on manual review of verb usage in operative notes. Stanford parser grammar production rules were also modified based on linguistic features of operative reports. An analogous approach was then applied to the GENIA corpus to test the generalizability of this approach to biologic text. RESULTS: The new unlexicalized PCFG parser extended with the extra lexicon from SPECIALIST along with accurate statistics collected from an operative note corpus tagged with GENIA POS tagger improved the F-score by 2.26% from 87.64% to 89.90%. There was a progressive improvement with the addition of multiple approaches. Lexicon augmentation combined with statistics from the operative notes corpus provided the greatest improvement of parser performance. Application of this approach on the GENIA corpus increased the F-score by 3.81% with a simple new grammar and addition of the GENIA corpus lexicon. CONCLUSION: Using statistics collected from clinical text tagged with POS taggers along with proper modification of grammars and lexicons of an unlexicalized PCFG parser may improve parsing performance of existing parsers on specialized clinical text.
Authors: Paul Thompson; Riza Theresa Batista-Navarro; Georgios Kontonatsios; Jacob Carter; Elizabeth Toon; John McNaught; Carsten Timmermann; Michael Worboys; Sophia Ananiadou Journal: PLoS One Date: 2016-01-06 Impact factor: 3.240