As an example, consider the following entities: Beijing, Pékin, and Pequim as expressed in English, French, and Portuguese respectively.
table 2: Characteristics of the generated PropBanks used for training the SRL models and the FrameNets used for evaluating the trained models We evaluated the quality of the generated PropBanks in a practical setting as well as the effectiveness of using entity-like LUs in addition to entities.