{"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/grammatical-error-detection-using-error-and","title":"Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings","arxiv_id":null,"date":"2017-11-01","proceeding":"IJCNLP 2017 11","authors":["Masahiro Kaneko","Yuya Sakaizawa","Mamoru Komachi"],"abstract":"In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns. Most existing algorithms for learning word embeddings usually model only the syntactic context of words so that classifiers treat erroneous and correct words as similar inputs. We address the problem of contextual information by considering learner errors. Specifically, we propose two models: one model that employs grammatical error patterns and another model that considers grammaticality of the target word. We determine grammaticality of n-gram sequence from the annotated error tags and extract grammatical error patterns for word embeddings from large-scale learner corpora. Experimental results show that a bidirectional long-short term memory model initialized by our word embeddings achieved the state-of-the-art accuracy by a large margin in an English grammatical error detection task on the First Certificate in English dataset.","url_abs":"https://aclanthology.org/I17-1005","url_pdf":"https://aclanthology.org/I17-1005.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":"grammatical-error-detection-using-error-and","repo_url":"https://github.com/kanekomasahiro/grammatical-error-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"grammatical-error-detection","task_name":"Grammatical Error Detection"},{"task_slug":"learning-word-embeddings","task_name":"Learning Word Embeddings"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/grammatical-error-detection-on-fce","task":"Grammatical Error Detection","dataset":"FCE","model":"Bi-LSTM+  Error- and Grammaticality-Specific Word Embeddings","rank_in_archive_order":6,"of":8,"metrics":{"F0.5":"44.6"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}