{"url":"/sota/lexical-normalization-on-lexnorm","task":{"name":"Lexical Normalization","url":"/task/lexical-normalization","note":null},"dataset":{"name":"LexNorm","url":null},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"Lexical normalization is the task of translating/transforming a non standard text to a standard register.\n\nExample:\n\n```\nnew pix comming tomoroe\nnew pictures coming tomorrow\n```\n\nDatasets usually consists of tweets, since these naturally contain a fair amount of \nthese phenomena.\n\nFor lexical normalization, only replacements on the word-level are annotated.\nSome corpora include annotation for 1-N and N-1 replacements. However, word\ninsertion/deletion and reordering is not part of the task.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher"}},"counts":{"rows":4,"rows_with_code":2,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"MoNoise","metrics":{"Accuracy":"87.63"},"uses_additional_data":false,"paper_date":"2017-10-10","paper":"/paper/monoise-modeling-noise-using-a-modular","paper_url":"http://arxiv.org/abs/1710.03476v1","paper_title":"MoNoise: Modeling Noise Using a Modular Normalization System","code":"https://github.com/wesselreijngoud/masterthesis2019","n_code_links":2,"syntology":null},{"rank_in_archive_order":2,"model":"Syllable based","metrics":{"Accuracy":"86.08"},"uses_additional_data":false,"paper_date":"2015-07-01","paper":"/paper/tweet-normalization-with-syllables","paper_url":"https://aclanthology.org/P15-1089","paper_title":"Tweet Normalization with Syllables","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":3,"model":"TextNorm","metrics":{"Accuracy":"83.94"},"uses_additional_data":false,"paper_date":"2019-04-12","paper":"/paper/adapting-sequence-to-sequence-models-for-text","paper_url":"http://arxiv.org/abs/1904.06100v1","paper_title":"Adapting Sequence to Sequence models for Text Normalization in Social Media","code":"https://github.com/Isminoula/TextNormSeq2Seq","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"unLOL","metrics":{"Accuracy":"82.06"},"uses_additional_data":false,"paper_date":"2013-10-01","paper":"/paper/a-log-linear-model-for-unsupervised-text","paper_url":"https://aclanthology.org/D13-1007","paper_title":"A Log-Linear Model for Unsupervised Text Normalization","code":null,"n_code_links":0,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}