Unsupervised Word Sense Disambiguation for Automatic Essay Scoring

Nedungadi, Prema; Raj, Harsha

doi:10.1007/978-3-319-07353-8_51

Cited by 8 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Owing to this relevance to different language assessment scenarios, AES has been widely studied by educational technology researchers. While most of the published research on AES has been on proprietary systems, recent availability of publicly accessible learner corpora facilitated comparable and replicable research on second language (L2) proficiency assessment (Yannakoudakis et al, 2011;Nedungadi and Raj, 2014).…”

Section: Introductionmentioning

confidence: 99%

Automated Assessment of Non-Native Learner Essays: Investigating the Role of Linguistic Features

Vajjala

2017

Int J Artif Intell Educ

View full text Add to dashboard Cite

Automatic essay scoring (AES) refers to the process of scoring free text responses to given prompts, considering human grader scores as the gold standard. Writing such essays is an essential component of many language and aptitude exams. Hence, AES became an active and established area of research, and there are many proprietary systems used in real life applications today. However, not much is known about which specific linguistic features are useful for prediction and how much of this is consistent across datasets. This article addresses that by exploring the role of various linguistic features in automatic essay scoring using two publicly available datasets of non-native English essays written in test taking scenarios. The linguistic properties are modeled by encoding lexical, syntactic, discourse and error types of learner language in the feature set. Predictive models are then developed using these features on both datasets and the most predictive features are compared. While the results show that the feature set used results in good predictive models with both datasets, the question "what are the most predictive features?" has a different answer for each dataset.

show abstract

Section: Introductionmentioning

confidence: 99%

Automated Assessment of Non-Native Learner Essays: Investigating the Role of Linguistic Features

Vajjala

2017

Int J Artif Intell Educ

View full text Add to dashboard Cite

show abstract

“…The metric was found to improve correlation measures between predicted and annotated scores. [16] used spelling errors in a simple regression model but the feature significance was not evaluated. [23] implemented error features in a classification model.…”

Section: Automatic Essay Scoring Systems and Second Language Learningmentioning

confidence: 99%

A Supervised Learning Model for the Automatic Assessment of Language Levels Based on Learner Errors

Ballier

Gaillat

Simpkin

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

This paper focuses on the use of technology in language learning. Language training requires the need to group learners homogeneously and to provide them with instant feedback on their productions such as errors [8, 15, 17] or proficiency levels. A possible approach is to assess writings from students and assign them with a level. This paper analyses the possibility of automatically predicting Common European Framework of Reference (CEFR) language levels on the basis of manually annotated errors in a written learner corpus [9, 11]. The research question is to evaluate the predictive power of errors in terms of levels and to identify which error types appear to be criterial features in determining interlanguage stages. Results show that specific errors such as punctuation, spelling and verb tense are significant at specific CEFR levels.

show abstract