Reading Level Assessment Using Support Vector Machines and Statistical Language Models
By Sarah E. Schwarm, Mari Ostendorf. Annual Meeting of the Association for Computational Linguistics, 2005
380 citations recorded by the source corpus.
Reading proficiency is a fundamental component of language competency. However, finding topical texts at an appropriate reading level for foreign and second language learners is a challenge for teachers. This task can be addressed with natural language processing technology to assess reading level. Existing measures of reading level are not well suited to this task, but previous work and our own pilot experiments have shown the benefit of using statistical language models. In this paper, we also use support vector machines to combine features from traditional reading level measures, statistical language models, and other language processing tools to produce a better method of assessing reading level.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/f6c035de37e797f7e5167d655b246a2f9426a9eb
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Citations in the corpus
- Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning (2016, background): “nd extend it to additional simplicity/complexity measures of training data. Our features are inspired by prior research in second language acquisition, text simplification, and readability assessment (Schwarm and Ostendorf, 2005; Heilman et al., 2007; Pitler and Nenkova, 2008; Vajjala and Meurers, 20”
- Revisiting Readability: A Unified Framework for Predicting Text Quality (2008, methodology): “186”
- A Monolingual Tree-based Translation Model for Sentence Simplification (2010, background): “Language models constitute an important feature for assessing readability (Schwarm and Ostendorf, 2005).”