{"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/eidos-indra-delphi-from-free-text-to","title":"Eidos, INDRA, \\& Delphi: From Free Text to Executable Causal Models","arxiv_id":null,"date":"2019-06-01","proceeding":"NAACL 2019 6","authors":["Rebecca Sharp","Adarsh Pyarelal","Benjamin Gyori","Keith Alcock","Egoitz Laparra","Marco A. Valenzuela-Esc{\\'a}rcega","Ajay Nagesh","Vikas Yadav","John Bachman","Zheng Tang","Heather Lent","Fan Luo","Mithun Paul","Steven Bethard","Kobus Barnard","Clayton Morrison","Mihai Surdeanu"],"abstract":"Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed approach is implemented through three systems: Eidos, INDRA, and Delphi. Eidos is an open-domain machine reading system designed to extract causal relations from natural language. It is rule-based, allowing for rapid domain transfer, customizability, and interpretability. INDRA aggregates multiple sources of causal information and performs assembly to create a coherent knowledge base and assess its reliability. This assembled knowledge serves as the starting point for modeling. Delphi is a modeling framework that assembles quantified causal fragments and their contexts into executable probabilistic models that respect the semantics of the original text, and can be used to support decision making.","url_abs":"https://aclanthology.org/N19-4008","url_pdf":"https://aclanthology.org/N19-4008.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":"eidos-indra-delphi-from-free-text-to","repo_url":"https://github.com/ml4ai/delphi","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}