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X-AMR Annotation Tool

Shafiuddin Rehan Ahmed, Jon Z. Cai, Martha Palmer, James H. Martin

TL;DR

A novel Cross-document Abstract Meaning Representation (X-AMR) annotation tool designed for annotating key corpus-level event semantics using the Prodigy Annotation Tool is presented, highlighting its advantages when integrated with GPT-4.

Abstract

This paper presents a novel Cross-document Abstract Meaning Representation (X-AMR) annotation tool designed for annotating key corpus-level event semantics. Leveraging machine assistance through the Prodigy Annotation Tool, we enhance the user experience, ensuring ease and efficiency in the annotation process. Through empirical analyses, we demonstrate the effectiveness of our tool in augmenting an existing event corpus, highlighting its advantages when integrated with GPT-4. Code and annotations: https://github.com/ahmeshaf/gpt_coref

X-AMR Annotation Tool

TL;DR

A novel Cross-document Abstract Meaning Representation (X-AMR) annotation tool designed for annotating key corpus-level event semantics using the Prodigy Annotation Tool is presented, highlighting its advantages when integrated with GPT-4.

Abstract

This paper presents a novel Cross-document Abstract Meaning Representation (X-AMR) annotation tool designed for annotating key corpus-level event semantics. Leveraging machine assistance through the Prodigy Annotation Tool, we enhance the user experience, ensuring ease and efficiency in the annotation process. Through empirical analyses, we demonstrate the effectiveness of our tool in augmenting an existing event corpus, highlighting its advantages when integrated with GPT-4. Code and annotations: https://github.com/ahmeshaf/gpt_coref
Paper Structure (18 sections, 7 figures, 1 table)

This paper contains 18 sections, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Annotation Methodology
  4. Annotation Guidelines for X-AMR
  5. PropBank & AMR
  6. Roleset Sense Annotation
  7. Document-level Arguments Identification
  8. ARG-Loc & ARG-Time Identification
  9. Annotation Interface
  10. Model-in-the-loop
  11. Analysis
  12. Model-in-the-loop
  13. GPT-in-the-loop
  14. Future Work
  15. Conclusion
  16. ...and 3 more sections

Figures (7)

  • Figure 1: The Annotation Methodology of X-AMR. The annotators are presented with PropBank and are allowed to use external resources, such as Wikipedia and Google News, during the annotations.
  • Figure 2: The PropBank roleset definitions of agree.01 and the expected annotations in X-AMR.
  • Figure 3: Eventive ARG-1 in the roleset agree.01. The ARG-1 clause is annotated as the connecting event with roleset ID acquire.01
  • Figure 4: The Annotation Interface Using prodi.gy Annotation Tool
  • Figure 5: Screenshots
  • ...and 2 more figures