Automatic readability classification of crowd-sourced data based on linguistic and information-theoretic features
|Automatic readability classification of crowd-sourced data based on linguistic and information-theoretic features|
|Author(s)||Islam Z., Mehler A.|
|Published in||Computacion y Sistemas|
|Keyword(s)||Enthropy, Evaluation of features, Information transmission, Text readability, Wikipedia|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of journal articles|
Automatic readability classification of crowd-sourced data based on linguistic and information-theoretic features is a 2013 journal article written in English by Islam Z., Mehler A. and published in Computacion y Sistemas.
This paper presents a classifier of text readability based on information-theoretic features. The classifier was developed based on a linguistic approach to readability that explores lexical, syntactic and semantic features. For this evaluation we extracted a corpus of 645 articles from Wikipedia together with their quality judgments. We show that information-theoretic features perform as well as their linguistic counterparts even if we explore several linguistic levels at once.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers. Cited 1 time(s)