Evidence map›Paper›PMID 29057293›Full record

ArticleJournal of the Association for Information Science and Technology2017

Measuring Text Difficulty Using Parse-Tree Frequency.

David Kauchak, Gondy Leroy, Alan Hogue

Abstract read
In one paragraph

Article in Journal of the Association for Information Science and Technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Compositional Complexity in Text and Images.Neurobiology of language (Cambridge, Mass.) · 2026
    Article
  6. Article
  7. Article
  8. APPLS: Evaluating Evaluation Metrics for Plain Language Summarization.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2024
    Article
  9. Text and Audio Simplification: Human vs. ChatGPT.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024
    Article
  10. Article
  11. Article
  12. Toward Improving Health Literacy in Patient Education Materials with Neural Machine Translation Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023
    Article
  13. Article
  14. Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

David KauchakComputer Science Department, Pomona College, Claremont, CA.
Gondy LeroyDepartment of Management Information Systems, Eller College of Management, University of Arizona, Tucson, AZ.
Alan HogueDepartment of Linguistics, University of Arizona, Tucson, AZ.

Funding

Evidence-based Strategy and Tool to Simplify Text for Patients and ConsumersR01LM011975 · NLM · UNIVERSITY OF ARIZONA · PI LEROY, GONDY · 2015 to 2024
$2.9M
NLM NIH HHS R01 LM011975
6 · The paper itself

Abstract

Text simplification often relies on dated, unproven readability formulas. As an alternative and motivated by the success of term familiarity, we test a complementary measure: grammar familiarity. Grammar familiarity is measured as the frequency of the 3

Indexed as

ComprehensionHealth LiteracyPatient EducationReadabilityText DifficultyText Simplification

Identifiers

PMID29057293
PMCPMC5644354

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.