Abstract
Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013) is a typologically-informed, broad-coverage semantic annotation scheme that describes coarse-grained predicate-argument structure but currently lacks semantic roles. We argue that lexicon-free annotation of the semantic roles marked by prepositions, as formulated by Schneider et al. (2018), is complementary and suitable for integration within UCCA. We show empirically for English that the schemes, though annotated independently, are compatible and can be combined in a single semantic graph. A comparison of several approaches to parsing the integrated representation lays the groundwork for future research on this task.
| Original language | American English |
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| Title of host publication | CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference |
| Publisher | Association for Computational Linguistics |
| Pages | 174-185 |
| Number of pages | 12 |
| ISBN (Electronic) | 9781950737727 |
| State | Published - 2019 |
| Event | 23rd Conference on Computational Natural Language Learning, CoNLL 2019 - Hong Kong, China Duration: 3 Nov 2019 → 4 Nov 2019 |
Publication series
| Name | CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference |
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Conference
| Conference | 23rd Conference on Computational Natural Language Learning, CoNLL 2019 |
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| Country/Territory | China |
| City | Hong Kong |
| Period | 3/11/19 → 4/11/19 |
Bibliographical note
Funding Information:We would like to thank Daniel Hershcovich and Adi Shalev for letting us use their code and helping fix bugs; Sean MacAvaney and Vivek Sriku-mar for assistance with computing resources; as well as three anonymous reviewers for their helpful comments and suggestions. This research was supported in part by NSF award IIS-1812778 and grant 2016375 from the United States–Israel Binational Science Foundation (BSF), Jerusalem, Israel.
Funding Information:
We would like to thank Daniel Hershcovich and Adi Shalev for letting us use their code and helping fix bugs; Sean MacAvaney and Vivek Srikumar for assistance with computing resources; as well as three anonymous reviewers for their helpful comments and suggestions. This research was supported in part by NSF award IIS-1812778 and grant 2016375 from the United States-Israel Binational Science Foundation (BSF), Jerusalem, Israel.
Publisher Copyright:
© 2019 Association for Computational Linguistics.