{"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/robust-named-entity-recognition-in","title":"Robust Named Entity Recognition in Idiosyncratic Domains","arxiv_id":"1608.06757","date":"2016-08-24","proceeding":null,"authors":["Sebastian Arnold","Felix A. Gers","Torsten Kilias","Alexander Löser"],"abstract":"Named entity recognition often fails in idiosyncratic domains. That causes a\nproblem for depending tasks, such as entity linking and relation extraction. We\npropose a generic and robust approach for high-recall named entity recognition.\nOur approach is easy to train and offers strong generalization over diverse\ndomain-specific language, such as news documents (e.g. Reuters) or biomedical\ntext (e.g. Medline). Our approach is based on deep contextual sequence learning\nand utilizes stacked bidirectional LSTM networks. Our model is trained with\nonly few hundred labeled sentences and does not rely on further external\nknowledge. We report from our results F1 scores in the range of 84-94% on\nstandard datasets.","url_abs":"http://arxiv.org/abs/1608.06757v1","url_pdf":"http://arxiv.org/pdf/1608.06757v1.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":"robust-named-entity-recognition-in","repo_url":"https://github.com/sebastianarnold/TeXoo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}