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Precision communication: Physicians’ linguistic adaptation to patients’ health literacy
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In: Sci Adv (2021)
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Multi-document Cohesion Network Analysis: Visualizing Intratextual and Intertextual Links
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In: Artificial Intelligence in Education (2020)
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Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study
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In: Health Serv Res (2020)
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Extended Multi-document Cohesion Network Analysis Centered on Comprehension Prediction
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In: Artificial Intelligence in Education (2020)
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Sequence-to-Sequence Models for Automated Text Simplification
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In: Artificial Intelligence in Education (2020)
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Challenges and solutions to employing natural language processing and machine learning to measure patients’ health literacy and physician writing complexity: The ECLIPPSE study
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In: J Biomed Inform (2020)
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Secure Messaging with Physicians by Proxies for Patients with Diabetes: Findings from the ECLIPPSE Study
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In: J Gen Intern Med (2019)
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Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality ...
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Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality ...
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To Aggregate or Not? Linguistic Features in Automatic Essay Scoring and Feedback Systems
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In: Journal of Writing Assessment, vol 8, iss 1 (2015)
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Abstract:
This study investigates the relative efficacy of using linguistic micro-features, the aggregation of such features, and a combination of micro-features and aggregated features in developing automatic essay scoring (AES) models. Although the use of aggregated features is widespread in AES systems (e.g., e-rater; Intellimetric), very little published data exists that demonstrates the superiority of using such a method over the use of linguistic micro-features or combination of both micro-features and aggregated features. The results of this study indicate that AES models comprised of micro-features and a combination of micro-features and aggregated features outperform AES models comprised of aggregated features alone. The results also indicate that that AES models based on micro-features and a combination of micro-features and aggregated features provide a greater variety of features with which to provide formative feedback to writers. These results have implications for the development of AES systems and for providing automatic feedback to writers within these systems.
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URL: https://escholarship.org/uc/item/1f21q8ck
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Linguistic microfeatures to predict L2 writing proficiency: A case study in Automated Writing Evaluation
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In: Journal of Writing Assessment, vol 7, iss 1 (2014)
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What's so simple about simplified texts? A computational and psycholinguistic investigation of text comprehension and text processing
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