AAAI Publications, The Twenty-Ninth International Flairs Conference

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Event Nugget Detection and Argument Extraction with DISCERN
Greg Dubbin, Archna Bhatia, Bonnie J. Dorr, Adam Dalton, Kristy Hollingshead, Ian Perera, Suriya Kandaswamy, Jena D. Hwang

Last modified: 2016-03-30


This paper addresses the problem of detecting information about events from unstructured text. An event-detection system, DISCERN, is presented; its three variants DISCERN- R (rule-based), DISCERN-ML (machine-learned), and DISCERN-C (combined), were evaluated in the NIST TAC KBP 2015 Event Nugget Detection and Event Argument Extraction and Linking tasks. Three contributions of this work are: (a) an approach to collapsing support verb and event nominals that improved recall of argument linking, (b) a new linguist-in-the-loop paradigm that enables quick changes to linguistic rules and examination of their effect on pre- cision and recall at runtime, (c) an analysis of the synergy between the semantic and syntactic features. Results of experimentation with event-detection approaches indicate that linguistically-informed rules can improve precision and machine-learned systems can improve recall. Future refinements to the combination of linguistic and machine learning approaches may involve making better use of the complementarity of these approaches.


Event detection, event extraction, event argument extraction, event argument linking, linguistic rules, machine learning

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