AutoAspect: Automatic Annotation of Tense and Aspect for Uniform Meaning Representations

Daniel Chen, Martha Palmer, Meagan Vigus


Abstract
We present AutoAspect, a novel, rule-based annotation tool for labeling tense and aspect. The pilot version annotates English data. The aspect labels are designed specifically for Uniform Meaning Representations (UMR), an annotation schema that aims to encode crosslingual semantic information. The annotation tool combines syntactic and semantic cues to assign aspects on a sentence-by-sentence basis, following a sequence of rules that each output a UMR aspect. Identified events proceed through the sequence until they are assigned an aspect. We achieve a recall of 76.17% for identifying UMR events and an accuracy of 62.57% on all identified events, with high precision values for 2 of the aspect labels.
Anthology ID:
2021.law-1.4
Original:
2021.law-1.4v1
Version 2:
2021.law-1.4v2
Volume:
Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
DMR | EMNLP | LAW
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
36–45
Language:
URL:
https://aclanthology.org/2021.law-1.4
DOI:
10.18653/v1/2021.law-1.4
Bibkey:
Cite (ACL):
Daniel Chen, Martha Palmer, and Meagan Vigus. 2021. AutoAspect: Automatic Annotation of Tense and Aspect for Uniform Meaning Representations. In Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop, pages 36–45, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
AutoAspect: Automatic Annotation of Tense and Aspect for Uniform Meaning Representations (Chen et al., LAW 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.law-1.4.pdf