{"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/fewer-features-perform-well-at-native","title":"Fewer features perform well at Native Language Identification task","arxiv_id":null,"date":"2017-09-01","proceeding":"WS 2017 9","authors":["Taraka Rama","{\\c{C}}a{\\u{g}}r{\\i} {\\c{C}}{\\\"o}ltekin"],"abstract":"This paper describes our results at the NLI shared task 2017. We participated in essays, speech, and fusion task that uses text, speech, and i-vectors for the task of identifying the native language of the given input. In the essay track, a linear SVM system using word bigrams and character 7-grams performed the best. In the speech track, an LDA classifier based only on i-vectors performed better than a combination system using text features from speech transcriptions and i-vectors. In the fusion task, we experimented with systems that used combination of i-vectors with higher order n-grams features, combination of i-vectors with word unigrams, a mean probability ensemble, and a stacked ensemble system. Our finding is that word unigrams in combination with i-vectors achieve higher score than systems trained with larger number of $n$-gram features. Our best-performing systems achieved F1-scores of 87.16{\\%}, 83.33{\\%} and 91.75{\\%} on the essay track, the speech track and the fusion track respectively.","url_abs":"https://aclanthology.org/W17-5028","url_pdf":"https://aclanthology.org/W17-5028.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":[],"tasks":[{"task_slug":"language-identification","task_name":"Language Identification"},{"task_slug":"native-language-identification","task_name":"Native Language Identification"}],"methods":[{"method_slug":"lda","method_name":"LDA"},{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/native-language-identification-on-italki-nli","task":"Native Language Identification","dataset":"italki NLI","model":"Tubasfs","rank_in_archive_order":1,"of":2,"metrics":{"Average F1":"0.5807"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}