Name
Title | ||
|---|---|---|
Improving perceived and actual text difficulty for health information consumers using semi-automated methods. |
Abstract | ||
|---|---|---|
We are developing algorithms for semi-automated simplification of medical text. Based on lexical and grammatical corpus analysis, we identified a new metric, term familiarity, to help estimate text difficulty. We developed an algorithm that uses term familiarity to identify difficult text and select easier alternatives from lexical resources such as WordNet, UMLS and Wiktionary. Twelve sentences were simplified to measure perceived difficulty using a 5-point Likert scale. Two documents were simplified to measure actual difficulty by posing questions with and without the text present (information understanding and retention). We conducted a user study by inviting participants (N=84) via Amazon Mechanical Turk. There was a significant effect of simplification on perceived difficulty (p<.001). We also saw slightly improved understanding with better question-answering for simplified documents but the effect was not significant (p=.097). Our results show how term familiarity is a valuable component in simplifying text in an efficient and scalable manner. |
Year | Venue | Keywords |
|---|---|---|
2012 | AMIA | unified medical language system,comprehension,analysis of variance,algorithms |
Field | DocType | Volume |
Information retrieval,Computer science,Corpus analysis,Natural language processing,Artificial intelligence,Health literacy,Likert scale,WordNet,Unified Medical Language System,Comprehension,Health information,Scalability | Conference | 2012 |
ISSN | Citations | PageRank |
1942-597X | 13 | 0.79 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gondy Leroy | 1 | 528 | 47.72 |
| James E. Endicott | 2 | 36 | 2.65 |
| Obay Mouradi | 3 | 26 | 2.13 |
| David Kauchak | 4 | 363 | 25.92 |
| Melissa L Just | 5 | 13 | 0.79 |