{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automated-assessment-of-non-native-learner","title":"Automated assessment of non-native learner essays: Investigating the role of linguistic features","arxiv_id":"1612.00729","date":"2016-12-02","proceeding":null,"authors":["Sowmya Vajjala"],"abstract":"Automatic essay scoring (AES) refers to the process of scoring free text\nresponses to given prompts, considering human grader scores as the gold\nstandard. Writing such essays is an essential component of many language and\naptitude exams. Hence, AES became an active and established area of research,\nand there are many proprietary systems used in real life applications today.\nHowever, not much is known about which specific linguistic features are useful\nfor prediction and how much of this is consistent across datasets. This article\naddresses that by exploring the role of various linguistic features in\nautomatic essay scoring using two publicly available datasets of non-native\nEnglish essays written in test taking scenarios. The linguistic properties are\nmodeled by encoding lexical, syntactic, discourse and error types of learner\nlanguage in the feature set. Predictive models are then developed using these\nfeatures on both datasets and the most predictive features are compared. While\nthe results show that the feature set used results in good predictive models\nwith both datasets, the question \"what are the most predictive features?\" has a\ndifferent answer for each dataset.","url_abs":"http://arxiv.org/abs/1612.00729v1","url_pdf":"http://arxiv.org/pdf/1612.00729v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automated-assessment-of-non-native-learner","repo_url":"https://bitbucket.org/nishkalavallabhi/ijaiedpapercode","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1612.00729","atlas_url":"https://app.syntology.ai/?focus=1612.00729","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}