Kobus Barnard


2019

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Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models
Rebecca Sharp | Adarsh Pyarelal | Benjamin Gyori | Keith Alcock | Egoitz Laparra | Marco A. Valenzuela-Escárcega | Ajay Nagesh | Vikas Yadav | John Bachman | Zheng Tang | Heather Lent | Fan Luo | Mithun Paul | Steven Bethard | Kobus Barnard | Clayton Morrison | Mihai Surdeanu
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

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.

2003

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Word Sense Disambiguation with Pictures
Kobus Barnard | Matthew Johnson | David Forsyth
Proceedings of the HLT-NAACL 2003 Workshop on Learning Word Meaning from Non-Linguistic Data