{"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/casict-tibetan-word-segmentation-system-for","title":"CASICT Tibetan Word Segmentation System for MLWS2017","arxiv_id":"1710.06112","date":"2017-10-17","proceeding":null,"authors":["Jiawei Hu","Qun Liu"],"abstract":"We participated in the MLWS 2017 on Tibetan word segmentation task, our\nsystem is trained in a unrestricted way, by introducing a baseline system and\n76w tibetan segmented sentences of ours. In the system character sequence is\nprocessed by the baseline system into word sequence, then a subword unit (BPE\nalgorithm) split rare words into subwords with its corresponding features,\nafter that a neural network classifier is adopted to token each subword into\n\"B,M,E,S\" label, in decoding step a simple rule is used to recover a final word\nsequence. The candidate system for submition is selected by evaluating the\nF-score in dev set pre-extracted from the 76w sentences. Experiment shows that\nthis method can fix segmentation errors of baseline system and result in a\nsignificant performance gain.","url_abs":"http://arxiv.org/abs/1710.06112v1","url_pdf":"http://arxiv.org/pdf/1710.06112v1.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":"casict-tibetan-word-segmentation-system-for","repo_url":"https://github.com/rsennrich/subword-nmt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}