Automated Essay Scoring Using Bayes' Theorem
By Lawrence M. Rudner, T. Liang. 2002
294 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/8f1f42f466694154c61ed2133b5de91f8292798d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Marek Rei
- Dimitrios Alikaniotis
- Fei Dong
- Jie Yang
- Helen Yannakoudakis
- Dadi Ramesh
- Ted Briscoe
- Semire Dikli
Citations in the corpus
- A New Dataset and Method for Automatically Grading ESOL Texts (2011, methodology): “The Bayesian Essay Test Scoring sYstem (BETSY) (Rudner and Liang, 2002) uses multinomial or Bernoulli Naive Bayes models to classify texts into different classes (e.g. pass/fail, grades A–”
- Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring (2017, methodology): “In the classi-fication scenario, scores are divided into several categories, each score or score range is regarded as one class and the ordinary classification models are employed such as Naive Bayes (NB) and SVMs (Larkey, 1998; Rudner and Liang, 2002).”
- An automated essay scoring systems: a systematic literature review (2021, background): “Powers et al. (2002) proposed E-rater and Intellimetric by Rudner et al. (2006) and Bayesian Essay Test Scoring System (BESTY) by Rudner and Liang (2002), these systems use natural language processing (NLP) techniques that focus on style and content to obtain the score of an essay.”
- An Examination of Decision-Theory Adaptive Testing Procedures (2009, methodology): “In the assessment arena, the model has been applied to essay scoring (Rudner & Liang, 2002) and diagnostic testing (Talento-Miller & Rudner, in preparation).”
- An Overview of Automated Scoring of Essays. (2006, methodology): “By minimizing entropy, it is possible to pick the items with maximum potential information gain (Rudner & Liang, 2002).”
- Automatic Text Scoring Using Neural Networks (2016, background): “…1968; At- tali and Burstein, 2006; Rudner and Liang, 2002; Elliot, 2003; Landauer et al., 2003; Briscoe et al., 2010; Yannakoudakis et al., 2011; Sakaguchi et al., 2015, among others), overviews of which can be found in various studies (Williamson, 2009; Dikli, 2006; Shermis and Hammer, 2012).”