| Autor: |
Ajay Nagesh, Zheng Tang, Vikas Yadav, Steven Bethard, Keith Alcock, Egoitz Laparra, Marco Antonio Valenzuela-Escárcega, Benjamin M. Gyori, Fan Luo, Mihai Surdeanu, Clayton T. Morrison, Adarsh Pyarelal, John A. Bachman, Rebecca Sharp, Heather C. Lent, Mithun Paul, Kobus Barnard |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
NAACL-HLT (Demonstrations) |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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