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We propose a generic, memory-based approach for the detection of implicit semantic roles. While state-of-the-art methods for this task combine hand-crafted rules with specialized and costly lexical resources, our models use large corpora with automated annotations for explicit semantic roles only to capture the distribution of predicates and their associated roles. We show that memory-based learning can increase the recognition rate of implicit roles beyond the state-of-the-art.

Memory-Based Acquisition of Argument Structures and its Application to Implicit Role Detection