{"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/choosing-the-right-word-using-bidirectional","title":"Choosing the Right Word: Using Bidirectional LSTM Tagger for Writing Support Systems","arxiv_id":"1901.02490","date":"2019-01-08","proceeding":null,"authors":["Victor Makarenkov","Lior Rokach","Bracha Shapira"],"abstract":"Scientific writing is difficult. It is even harder for those for whom English\nis a second language (ESL learners). Scholars around the world spend a\nsignificant amount of time and resources proofreading their work before\nsubmitting it for review or publication.\n  In this paper we present a novel machine learning based application for\nproper word choice task. Proper word choice is a generalization the lexical\nsubstitution (LS) and grammatical error correction (GEC) tasks. We demonstrate\nand evaluate the usefulness of applying bidirectional Long Short Term Memory\n(LSTM) tagger, for this task. While state-of-the-art grammatical error\ncorrection uses error-specific classifiers and machine translation methods, we\ndemonstrate an unsupervised method that is based solely on a high quality text\ncorpus and does not require manually annotated data. We use a bidirectional\nRecurrent Neural Network (RNN) with LSTM for learning the proper word choice\nbased on a word's sentential context. We demonstrate and evaluate our\napplication on both a domain-specific (scientific), writing task and a\ngeneral-purpose writing task. We show that our domain-specific and\ngeneral-purpose models outperform state-of-the-art general context learning. As\nan additional contribution of this research, we also share our code,\npre-trained models, and a new ESL learner test set with the research community.","url_abs":"http://arxiv.org/abs/1901.02490v1","url_pdf":"http://arxiv.org/pdf/1901.02490v1.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":"choosing-the-right-word-using-bidirectional","repo_url":"https://github.com/vicmak/Exploiting-BiLSTM-for-Proper-Word-Choice","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"grammatical-error-correction","task_name":"Grammatical Error Correction"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}