Superframes: A Schema and Data for Universal Semantic Relation Annotation

• Applicant: Dr. Kilian Evang
• Subject Area: Applied Linguistics, Computational Linguistics
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 560341082

Existing schemas for semantic role labeling (SRL) rely on large lexicons, which makes annotation slow and hampers the adoption of SRL as an everyday tool for corpus linguists and NLP practitioners, the way syntactic parsing has become. I argue that decompositional representations of the meaning of predicates as proposed in some linguistic theories offer a way out of this problem, enabling semantic role annotation and prediction for all languages without the need for language-specific lexicons. But such representations first have to be made practical for the use in large-scale annotation and NLP. In particular, the number of primitive relations used in decompositional representations as well as their structural complexity has to be constrained. In the proposed project, I venture to define such a practical schema as an extension of dependency syntax and use it to annotate existing lexicons as well as large quantities of text in different languages. Based on this data, I propose to develop machine learning models for SRL as well as downstream tasks, and to show that SRL based on decompositional meaning representations improves upon traditional SRL in terms of inter-annotator agreement and machine learning performance.

• DFG Programme: Research Grants