We present a corpus which converts the sense labels of existing Propbank resources to a new unified format which is more compatible with AMR and more robust to sparsity. This adopts an innovation of the Abstract Meaning Representation project(Banarescu et al. 2013) in which one abstracts away from different, related parts of speech, so that related forms such as "insert" and "insertion" could be represented by the same roleset and use the same semantic roles. We note that this conversion also serves to make the different English Propbank corpora released over the years consistent with each other, so that one might train and evaluate systems upon that larger combined data. We present analysis of some appealing characteristics of this final dataset, and present preliminary results of training and evaluating SRL systems on this combined set, to spur usage of this challenging new dataset.
@InProceedings{O'GORMAN18.1074, author = {Tim O'Gorman and Sameer Pradhan and Martha Palmer and Julia Bonn and Kathryn Conger and James Gung}, title = "{The New Propbank: Aligning Propbank with AMR through POS Unification}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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