Feature-Based Assessment of Text Readability

Zhang, Lixiao; Liu, Zaiying; Ni, Jun

doi:10.1109/icicse.2013.18

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The prediction of proficiency levels for L2 teaching materials using supervised machine learning methods has been explored for English (Heilman et al, 2007;Huang et al, 2011;Zhang et al, 2013;Salesky and Shen, 2014;Xia et al, 2016), French (François and Fairon, 2012), Portuguese (Branco et al, 2014), Chinese (Sung et al, 2015) and, without the use of NLP, for Dutch (Velleman and van der Geest, 2014).…”

Section: Readability and Proficiency Level Classificationmentioning

confidence: 99%

Candidate sentence selection for language learning exercises: from a comprehensive framework to an empirical evaluation

Pilán,

Volodina,

Borin

2017

Preprint

View full text Add to dashboard Cite

We present a framework and its implementation relying on Natural Language Processing methods, which aims at the identification of exercise item candidates from corpora. The hybrid system combining heuristics and machine learning methods includes a number of relevant selection criteria. We focus on two fundamental aspects: linguistic complexity and the dependence of the extracted sentences on their original context. Previous work on exercise generation addressed these two criteria only to a limited extent, and a refined overall candidate sentence selection framework appears also to be lacking. In addition to a detailed description of the system, we present the results of an empirical evaluation conducted with language teachers and learners which indicate the usefulness of the system for educational purposes. We have integrated our system into a freely available online learning platform.

show abstract

Section: Readability and Proficiency Level Classificationmentioning

confidence: 99%

Candidate sentence selection for language learning exercises: from a comprehensive framework to an empirical evaluation

Pilán,

Volodina,

Borin

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Our focus, however, is on building a model for predicting the proficiency level of texts and sentences used in L2 teaching materials. This aspect has been explored for English [10][11][12][13], French [14], Portuguese [15] and, without the use of NLP, for Dutch [16].…”

Section: Introductionmentioning

confidence: 99%

A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity

Pilán,

Vajjala,

Volodina

2016

Preprint

View full text Add to dashboard Cite

Corpora and web texts can become a rich language learning resource if we have a means of assessing whether they are linguistically appropriate for learners at a given proficiency level. In this paper, we aim at addressing this issue by presenting the first approach for predicting linguistic complexity for Swedish second language learning material on a 5-point scale. After showing that the traditional Swedish readability measure, Läsbarhetsindex (LIX), is not suitable for this task, we propose a supervised machine learning model, based on a range of linguistic features, that can reliably classify texts according to their difficulty level. Our model obtained an accuracy of 81.3% and an F-score of 0.8, which is comparable to the state of the art in English and is considerably higher than previously reported results for other languages. We further studied the utility of our features with single sentences instead of full texts since sentences are a common linguistic unit in language learning exercises. We trained a separate model on sentence-level data with five classes, which yielded 63.4% accuracy. Although this is lower than the document level performance, we achieved an adjacent accuracy of 92%. Furthermore, we found that using a combination of different features, compared to using lexical features alone, resulted in 7% improvement in classification accuracy at the sentence level, whereas at the document level, lexical features were more dominant. Our models are intended for use in a freely accessible web-based language learning platform for the automatic generation of exercises.

show abstract

Feature-Based Assessment of Text Readability

Cited by 2 publications

References 6 publications

Candidate sentence selection for language learning exercises: from a comprehensive framework to an empirical evaluation

Candidate sentence selection for language learning exercises: from a comprehensive framework to an empirical evaluation

A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity

Contact Info

Product

Resources

About