{"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/a-study-of-n-gram-and-embedding","title":"A study of N-gram and Embedding Representations for Native Language Identification","arxiv_id":null,"date":"2017-09-01","proceeding":"WS 2017 9","authors":["Sowmya Vajjala","Sagnik Banerjee"],"abstract":"We report on our experiments with N-gram and embedding based feature representations for Native Language Identification (NLI) as a part of the NLI Shared Task 2017 (team name: NLI-ISU). Our best performing system on the test set for written essays had a macro F1 of 0.8264 and was based on word uni, bi and trigram features. We explored n-grams covering word, character, POS and word-POS mixed representations for this task. For embedding based feature representations, we employed both word and document embeddings. We had a relatively poor performance with all embedding representations compared to n-grams, which could be because of the fact that embeddings capture semantic similarities whereas L1 differences are more stylistic in nature.","url_abs":"https://aclanthology.org/W17-5026","url_pdf":"https://aclanthology.org/W17-5026.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":"a-study-of-n-gram-and-embedding","repo_url":"https://github.com/nishkalavallabhi/NLIST2017","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"language-acquisition","task_name":"Language Acquisition"},{"task_slug":"language-identification","task_name":"Language Identification"},{"task_slug":"native-language-identification","task_name":"Native Language Identification"},{"task_slug":"pos","task_name":"POS"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/native-language-identification-on-italki-nli","task":"Native Language Identification","dataset":"italki NLI","model":"NLI-ISU","rank_in_archive_order":2,"of":2,"metrics":{"Average F1":"0.5035"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}