NegAIT: A new parser for medical text simplification using morphological, sentential and double negation

By Partha Mukherjee, Gondy Leroy, David Kauchak, Srinidhi Rajanarayanan, Damian Y. Romero Diaz, Nicole P Yuan, T. Gail Pritchard, Sonia Colina. 2017

31 citations recorded by the source corpus.

Many different text features influence text readability and content comprehension. Negation is commonly suggested as one such feature, but few general-purpose tools exist to discover negation and studies of the impact of negation on text readability are rare. In this paper, we introduce a new negation parser (NegAIT) for detecting morphological, sentential, and double negation. We evaluated the parser using a human annotated gold standard containing 500 Wikipedia sentences and achieved 95%, 89% and 67% precision with 100%, 80%, and 67% recall, respectively. We also investigate two applications of this new negation parser. First, we performed a corpus statistics study to demonstrate different negation usage in easy and difficult text. Negation usage was compared in six corpora: patient blogs (4 K sentences), Cochrane reviews (91 K sentences), PubMed abstracts (20 K sentences), clinical trial texts (48 K sentences), and English and Simple English Wikipedia articles for different medical topics (60 K and 6 K sentences). The most difficult text contained the least negation. However, when comparing negation types, difficult texts (i.e., Cochrane, PubMed, English Wikipedia and clinical trials) contained significantly (p < 0.01) more morphological negations. Second, we conducted a predictive analytics study to show the importance of negation in distinguishing between easy and difficulty text. Five binary classifiers (Naïve Bayes, SVM, decision tree, logistic regression and linear regression) were trained using only negation information. All classifiers achieved better performance than the majority baseline. The Naïve Bayes’ classifier achieved the highest accuracy at 77% (9% higher than the majority baseline).

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Zexian Zeng
2. Tristan Naumann
3. Xiaoyu Li
4. Yu Deng
5. Yuan Luo

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “explored other methods such as random forests [112], decision trees [100], [113], [131], [132], and the Longitudinal Gamma Poisson Shrinker [133], [134] for computational phe-”