{"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/automatic-labeling-for-entity-extraction-in","title":"Automatic Labeling for Entity Extraction in Cyber Security","arxiv_id":"1308.4941","date":"2013-08-22","proceeding":null,"authors":["Robert A. Bridges","Corinne L. Jones","Michael D. Iannacone","Kelly M. Testa","John R. Goodall"],"abstract":"Timely analysis of cyber-security information necessitates automated\ninformation extraction from unstructured text. While state-of-the-art\nextraction methods produce extremely accurate results, they require ample\ntraining data, which is generally unavailable for specialized applications,\nsuch as detecting security related entities; moreover, manual annotation of\ncorpora is very costly and often not a viable solution. In response, we develop\na very precise method to automatically label text from several data sources by\nleveraging related, domain-specific, structured data and provide public access\nto a corpus annotated with cyber-security entities. Next, we implement a\nMaximum Entropy Model trained with the average perceptron on a portion of our\ncorpus ($\\sim$750,000 words) and achieve near perfect precision, recall, and\naccuracy, with training times under 17 seconds.","url_abs":"http://arxiv.org/abs/1308.4941v3","url_pdf":"http://arxiv.org/pdf/1308.4941v3.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":"automatic-labeling-for-entity-extraction-in","repo_url":"https://github.com/stucco/auto-labeled-corpus","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"automatic-labeling-for-entity-extraction-in","repo_url":"https://github.com/IS5882/Open-CyKG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"automatic-labeling-for-entity-extraction-in","repo_url":"https://github.com/ShashSec/SMTI_SA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"entity-extraction","task_name":"Entity Extraction using GAN"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1308.4941","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}