1 |
Precision communication: Physicians’ linguistic adaptation to patients’ health literacy
|
|
|
|
In: Sci Adv (2021)
|
|
BASE
|
|
Show details
|
|
2 |
Multi-document Cohesion Network Analysis: Visualizing Intratextual and Intertextual Links
|
|
|
|
In: Artificial Intelligence in Education (2020)
|
|
Abstract:
Reading comprehension requires readers to connect ideas within and across texts to produce a coherent mental representation. One important factor in that complex process regards the cohesion of the document(s). Here, we tackle the challenge of providing researchers and practitioners with a tool to visualize text cohesion both within (intra) and between (inter) texts. This tool, Multi-document Cohesion Network Analysis (MD-CNA), expands the structure of a CNA graph with lexical overlap links of multiple types, together with coreference links to highlight dependencies between text fragments of different granularities. We introduce two visualizations of the CNA graph that support the visual exploration of intratextual and intertextual links. First, a hierarchical view displays a tree-structure of discourse as a visual illustration of CNA links within a document. Second, a grid view available at paragraph or sentence levels displays links both within and between documents, thus ensuring ease of visualization for links spanning across multiple documents. Two use cases are provided to evaluate key functionalities and insights for each type of visualization.
|
|
Keyword:
Article
|
|
URL: https://doi.org/10.1007/978-3-030-52240-7_15 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334671/
|
|
BASE
|
|
Hide details
|
|
3 |
Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study
|
|
|
|
In: Health Serv Res (2020)
|
|
BASE
|
|
Show details
|
|
4 |
Extended Multi-document Cohesion Network Analysis Centered on Comprehension Prediction
|
|
|
|
In: Artificial Intelligence in Education (2020)
|
|
BASE
|
|
Show details
|
|
5 |
Sequence-to-Sequence Models for Automated Text Simplification
|
|
|
|
In: Artificial Intelligence in Education (2020)
|
|
BASE
|
|
Show details
|
|
6 |
Challenges and solutions to employing natural language processing and machine learning to measure patients’ health literacy and physician writing complexity: The ECLIPPSE study
|
|
|
|
In: J Biomed Inform (2020)
|
|
BASE
|
|
Show details
|
|
7 |
Secure Messaging with Physicians by Proxies for Patients with Diabetes: Findings from the ECLIPPSE Study
|
|
|
|
In: J Gen Intern Med (2019)
|
|
BASE
|
|
Show details
|
|
9 |
Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
To Aggregate or Not? Linguistic Features in Automatic Essay Scoring and Feedback Systems
|
|
|
|
In: Journal of Writing Assessment, vol 8, iss 1 (2015)
|
|
BASE
|
|
Show details
|
|
19 |
Linguistic microfeatures to predict L2 writing proficiency: A case study in Automated Writing Evaluation
|
|
|
|
In: Journal of Writing Assessment, vol 7, iss 1 (2014)
|
|
BASE
|
|
Show details
|
|
20 |
What's so simple about simplified texts? A computational and psycholinguistic investigation of text comprehension and text processing
|
|
|
|
BASE
|
|
Show details
|
|
|
|