Explanation Knowledge Graph Construction Through Causality Extraction from Texts |
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Authors: | Chaveevan Pechsiri Rapepun Piriyakul |
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Affiliation: | (1) Núcleo Interinstitucional de Lingüística Computacional (NILC), ICMC-University of São Paulo (USP), CP 668, 13560-970 São Carlos, SP, Brazil;(2) Instituto de Física de São Carlos, USP, CP 369, 13560-970 São Carlos, SP, Brazil |
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Abstract: | Explanation knowledge expressed by a graph, especially in the graphical model, is essential to comprehend clearly all paths
of effect events in causality for basic diagnosis. This research focuses on determining the effect boundary using a statistical
based approach and patterns of effect events in the graph whether they are consequence or concurrence without temporal markers.
All necessary causality events from texts for the graph construction are extracted on multiple clauses/EDUs (Elementary Discourse
Units) which assist in determining effect-event patterns from written event sequences in documents. To extract the causality
events from documents, it has to face the effect-boundary determination problems after applying verb pair rules (a causative
verb and an effect verb) to identify the causality. Therefore, we propose Bayesian Network and Maximum entropy to determine
the boundary of the effect EDUs. We also propose learning the effect-verb order pairs from the adjacent effect EDUs to solve
the effect-event patterns for representing the extracted causality by the graph construction. The accuracy result of the explanation
knowledge graph construction is 90% based on expert judgments whereas the average accuracy results from the effect boundary
determination by Bayesian Network and Maximum entropy are 90% and 93%, respectively. |
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