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.