{"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/towards-easier-and-faster-sequence-labeling","title":"Towards Easier and Faster Sequence Labeling for Natural Language Processing: A Search-based Probabilistic Online Learning Framework (SAPO)","arxiv_id":"1503.08381","date":"2015-03-29","proceeding":null,"authors":["Xu Sun","Shuming Ma","Yi Zhang","Xuancheng Ren"],"abstract":"There are two major approaches for sequence labeling. One is the\nprobabilistic gradient-based methods such as conditional random fields (CRF)\nand neural networks (e.g., RNN), which have high accuracy but drawbacks: slow\ntraining, and no support of search-based optimization (which is important in\nmany cases). The other is the search-based learning methods such as structured\nperceptron and margin infused relaxed algorithm (MIRA), which have fast\ntraining but also drawbacks: low accuracy, no probabilistic information, and\nnon-convergence in real-world tasks. We propose a novel and \"easy\" solution, a\nsearch-based probabilistic online learning method, to address most of those\nissues. The method is \"easy\", because the optimization algorithm at the\ntraining stage is as simple as the decoding algorithm at the test stage. This\nmethod searches the output candidates, derives probabilities, and conducts\nefficient online learning. We show that this method with fast training and\ntheoretical guarantee of convergence, which is easy to implement, can support\nsearch-based optimization and obtain top accuracy. Experiments on well-known\ntasks show that our method has better accuracy than CRF and BiLSTM\\footnote{The\nSAPO code is released at \\url{https://github.com/lancopku/SAPO}.}.","url_abs":"http://arxiv.org/abs/1503.08381v4","url_pdf":"http://arxiv.org/pdf/1503.08381v4.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":"towards-easier-and-faster-sequence-labeling","repo_url":"https://github.com/lancopku/Decode-CRF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"towards-easier-and-faster-sequence-labeling","repo_url":"https://github.com/lancopku/SAPO","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"towards-easier-and-faster-sequence-labeling","repo_url":"https://github.com/bratao/PySeqLab","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"towards-easier-and-faster-sequence-labeling","repo_url":"https://github.com/uzh-dqbm-cmi/PySeqLab","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[{"method_slug":"crf","method_name":"CRF"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}