{"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-building-a-knowledge-base-of-monetary","title":"Towards Building a Knowledge Base of Monetary Transactions from a News Collection","arxiv_id":"1709.05743","date":"2017-09-18","proceeding":null,"authors":["Jan R. Benetka","Krisztian Balog","Kjetil Nørvåg"],"abstract":"We address the problem of extracting structured representations of economic\nevents from a large corpus of news articles, using a combination of natural\nlanguage processing and machine learning techniques. The developed techniques\nallow for semi-automatic population of a financial knowledge base, which, in\nturn, may be used to support a range of data mining and exploration tasks. The\nkey challenge we face in this domain is that the same event is often reported\nmultiple times, with varying correctness of details. We address this challenge\nby first collecting all information pertinent to a given event from the entire\ncorpus, then considering all possible representations of the event, and\nfinally, using a supervised learning method, to rank these representations by\nthe associated confidence scores. A main innovative element of our approach is\nthat it jointly extracts and stores all attributes of the event as a single\nrepresentation (quintuple). Using a purpose-built test set we demonstrate that\nour supervised learning approach can achieve 25% improvement in F1-score over\nbaseline methods that consider the earliest, the latest or the most frequent\nreporting of the event.","url_abs":"http://arxiv.org/abs/1709.05743v1","url_pdf":"http://arxiv.org/pdf/1709.05743v1.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-building-a-knowledge-base-of-monetary","repo_url":"https://github.com/benetka/kbmt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}