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This paper introduces GLAMR, an Abstract Meaning Representation (AMR) interpretation of Generative Lexicon (GL) semantic components. It includes a structured subeventual interpretation of linguistic predicates, and encoding of the opposition structure of property changes of event arguments. Both of these features are recently encoded in VerbNet (VN), and form the scaffolding for the semantic form associated with VN frame files. We develop a new syntax, concepts, and roles for subevent structure based on VN for connecting subevents to atomic predicates. Our proposed extension is compatible with current AMR specification. We also present an approach to automatically augment AMR graphs by inserting subevent structure of the predicates and identifying the subevent arguments from the semantic roles. A pilot annotation of GLAMR graphs of 65 documents (486 sentences), based on procedural texts as a source, is presented as a public dataset. The annotation includes subevents, argument property change, and document-level anaphoric links. Finally, we provide baseline models for converting text to GLAMR and vice versa, along with the application of GLAMR for generating enriched paraphrases with details on subevent transformation and arguments that are not present in the surface form of the texts.
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Made for Each Other: Broad-Coverage Semantic Structures Meet Preposition Supersenses
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.
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- Award ID(s):
- 1812778
- PAR ID:
- 10190625
- Date Published:
- Journal Name:
- Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
- Page Range / eLocation ID:
- 174 to 185
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/K19-1017
