{"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/character-composition-model-with","title":"Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages","arxiv_id":"1705.10814","date":"2017-05-30","proceeding":"ACL 2017 7","authors":["Xiang Yu","Ngoc Thang Vu"],"abstract":"We present a transition-based dependency parser that uses a convolutional\nneural network to compose word representations from characters. The character\ncomposition model shows great improvement over the word-lookup model,\nespecially for parsing agglutinative languages. These improvements are even\nbetter than using pre-trained word embeddings from extra data. On the SPMRL\ndata sets, our system outperforms the previous best greedy parser (Ballesteros\net al., 2015) by a margin of 3% on average.","url_abs":"http://arxiv.org/abs/1705.10814v1","url_pdf":"http://arxiv.org/pdf/1705.10814v1.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":"character-composition-model-with","repo_url":"https://github.com/EggplantElf/sclem2017-tagger","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"dependency-parsing","task_name":"Dependency Parsing"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}