ArticleIEEE journal of biomedical and health informatics2019
Using Lexical Chains to Identify Text Difficulty: A Corpus Statistics and Classification Study.
Article in IEEE journal of biomedical and health informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Challenges and Opportunities for Professional Medical Publications Writers to Contribute to Plain Language Summaries (PLS) in an AI/ML Environment - A Consumer Health Informatics Systematic Review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Pooled it
- Parallel Corpus Analysis of Text and Audio Comprehension to Evaluate Readability Formula Effectiveness: Quantitative Analysis.Journal of medical Internet research · 2025Article
- Influence of Audio Speech Rate and Source Text Difficulty on Health Information Comprehension and Retention.Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences · 2024Article
- Text and Audio Simplification: Human vs. ChatGPT.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Effects of Added Emphasis and Pause in Audio Delivery of Health Information.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Assessing the Readability of Online Patient Education Materials in Obstetrics and Gynecology Using Traditional Measures: Comparative Analysis and Limitations.Journal of medical Internet research · 2023Article
- Audio delivery of health information: An NLP study of information difficulty and bias in listeners.Procedia computer science · 2023Article
- Evaluation of an online text simplification editor using manual and automated metrics for perceived and actual text difficulty.JAMIA open · 2022Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Our goal is data-driven discovery of features for text simplification. In this paper, we investigate three types of lexical chains: exact, synonymous, and semantic. A lexical chain links semantically related words in a document. We examine their potential with a document-level corpus statistics study (914 texts) to estimate their overall capacity to differentiate between easy and difficult text and a classification task (11 000 sentences) to determine usefulness of features at sentence-level for simplification. For the corpus statistics study we tested five document-level features for each chain type: total number of chains, average chain length, average chain span, number of crossing chains, and the number of chains longer than half the document length. We found significant differences between easy and difficult text for average chain length and the average number of cross chains. For the sentence classification study, we compared the lexical chain features to standard bag-of-words features on a range of classifiers: logistic regression, naïve Bayes, decision trees, linear and RBF kernel SVM, and random forest. The lexical chain features performed significantly better than the bag-of-words baseline across all classifiers with the best classifier achieving an accuracy of ∼90% (compared to 78% for bag-of-words). Overall, we find several lexical chain features provide specific information useful for identifying difficult sentences of text, beyond what is available from standard lexical features.
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Registered trials
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