Draw mir a Sheep: A Supersense-based Analysis of German Case and Adposition Semantics

Abstract

Adpositions and case markers are ubiquitous in natural language and express a wide range of meaning relations that can be of crucial relevance for many NLP and AI tasks. However, capturing their semantics in a comprehensive yet concise, as well as cross-linguistically applicable way has remained a challenge over the years. To address this, we adapt the largely language-agnostic SNACS framework to German, defining language-specific criteria for identifying adpositional expressions and piloting a supersense-annotated German corpus. We compare our approach with prior work on both German and multilingual adposition semantics, and discuss our empirical findings in the context of potential applications.

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Notes

  1. 1.

    For example, the verb zeichnen ‘to draw’ may evoke the specific frame Create_Physical_Artwork with the frame elements Creator and Representation, which are typically instantiated by the syntactic subject and object, respectively.

  2. 2.

    These are mostly closed-class; a list is provided to annotators.

  3. 3.

    In a purely illustrative sense. We are agnostic with respect to the formal syntactic notion of movement or any other syntactic paradigms.

  4. 4.

    In order to efficiently gather supersense-annotated data on a larger scale, the target identification step may be automated. Schneider et al. [61] have demonstrated for English that this is possible, at least approximately, based on part-of-speech tags and syntactic dependencies. We expect their method to be applicable to German as well, but this still needs to be tested empirically.

  5. 5.

    The moderate agreement also suggests that the task in its current form is likely too difficult for crowd workers without either a background in linguistics or close supervision by a project manager. Alternative task formulations that facilitate crowdsourcing have been proposed by Gessler et al. [20].

  6. 6.

    These thought experiments are in their style and purpose inspired by Trott et al. [70].

  7. 7.

    Google Translate

  8. 8.

    The examples here are cherry-picked, and especially sentence 4 requires specific background knowledge, without which no automatic or human translator is expected to generate the intended output. However, by demonstrating that subtle differences in lexical meaning—even and especially that of adpositions—can have a dramatic impact on sentence meaning, we argue that translation systems that lack semantic awareness and nuance are inherently unreliable and prone to inconsistencies.

  9. 9.

    mal is a modal particle establishing that the scene ‘just happens to unfold this way’.

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Acknowledgements

We wish to thank our annotators Lea Seiler, Christoph Otto, Barbara Baeuerle, Ryan Mannion, and Cornelia Zorca for their commitment and care in carrying out their work. The development of the annotation scheme presented here has greatly benefitted from discussions between the authors and Omri Abend, Aryaman Arora, Jena Hwang, Emma Manning, Vivek Srikumar, and Brandon Tullock. We are also thankful to two anonymous reviewers for their questions and suggestions.

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Correspondence to Jakob Prange.

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This research was supported in part by the National Science Foundation of the United States of America (NSF award IIS-1812778) and the United States–Israel Binational Science Foundation, Jerusalem, Israel (BSF grant 2016375)

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Prange, J., Schneider, N. Draw mir a Sheep: A Supersense-based Analysis of German Case and Adposition Semantics. Künstl Intell (2021). https://doi.org/10.1007/s13218-021-00712-y

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Keywords

  • Natural language understanding
  • Prepositions
  • Lexical semantics
  • Case
  • German