{"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/capturing-long-range-contextual-dependencies","title":"Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields","arxiv_id":"1709.03637","date":"2017-09-12","proceeding":"IJCNLP 2017 11","authors":["Fei Liu","Timothy Baldwin","Trevor Cohn"],"abstract":"Despite successful applications across a broad range of NLP tasks,\nconditional random fields (\"CRFs\"), in particular the linear-chain variant, are\nonly able to model local features. While this has important benefits in terms\nof inference tractability, it limits the ability of the model to capture\nlong-range dependencies between items. Attempts to extend CRFs to capture\nlong-range dependencies have largely come at the cost of computational\ncomplexity and approximate inference. In this work, we propose an extension to\nCRFs by integrating external memory, taking inspiration from memory networks,\nthereby allowing CRFs to incorporate information far beyond neighbouring steps.\nExperiments across two tasks show substantial improvements over strong CRF and\nLSTM baselines.","url_abs":"http://arxiv.org/abs/1709.03637v2","url_pdf":"http://arxiv.org/pdf/1709.03637v2.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":"capturing-long-range-contextual-dependencies","repo_url":"https://github.com/liufly/mecrf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"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}