Frame Semantics Annotation Made Easy with DBpedia

Crowdsourcing techniques applied to natural language processing have recently experienced a steady growth and represent a cheap and fast, albeit valid, solution to create benchmarks and training data. Nevertheless, some particularly complex tasks such as semantic role annotation have been rarely conducted in a crowdsourcing environment, due to their intrinsic difficulty. In this paper, we present a novel approach to accomplish this task by leveraging information automatically extracted from DBpedia. We show that replacing role definitions, typically meant for expert annotators, with a list of DBpedia types, makes the identification and assignment of role labels more intuitive also for non-expert workers. Results prove that such strategy improves on the standard annotation workflow, both in terms of accuracy and of time consumption.