@inproceedings{cai-etal-2025-lidarr,
title = "{L}i{DARR}: Linking Document {AMR}s with Referents Resolvers",
author = "Cai, Jon and
Wright-Bettner, Kristin and
Zhao, Zekun and
Ahmed, Shafiuddin Rehan and
Ramachandran, Abijith Trichur and
Flanigan, Jeffrey and
Palmer, Martha and
Martin, James",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.41/",
doi = "10.18653/v1/2025.acl-demo.41",
pages = "426--435",
ISBN = "979-8-89176-253-4",
abstract = "In this paper, we present LiDARR (**Li**nking **D**ocument **A**MRs with **R**eferents **R**esolvers), a web tool for semantic annotation at the document level using the formalism of Abstract Meaning Representation (AMR). LiDARR streamlines the creation of comprehensive knowledge graphs from natural language documents through semantic annotation. The tool features a visualization and interactive user interface, transforming document-level AMR annotation into an models-facilitated verification process. This is achieved through the integration of an AMR-to-surface alignment model and a coreference resolution model. Additionally, we incorporate PropBank rolesets into LiDARR to extend implicit roles in annotated AMR, allowing implicit roles to be linked through the coreference chains via AMRs."
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%0 Conference Proceedings
%T LiDARR: Linking Document AMRs with Referents Resolvers
%A Cai, Jon
%A Wright-Bettner, Kristin
%A Zhao, Zekun
%A Ahmed, Shafiuddin Rehan
%A Ramachandran, Abijith Trichur
%A Flanigan, Jeffrey
%A Palmer, Martha
%A Martin, James
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F cai-etal-2025-lidarr
%X In this paper, we present LiDARR (**Li**nking **D**ocument **A**MRs with **R**eferents **R**esolvers), a web tool for semantic annotation at the document level using the formalism of Abstract Meaning Representation (AMR). LiDARR streamlines the creation of comprehensive knowledge graphs from natural language documents through semantic annotation. The tool features a visualization and interactive user interface, transforming document-level AMR annotation into an models-facilitated verification process. This is achieved through the integration of an AMR-to-surface alignment model and a coreference resolution model. Additionally, we incorporate PropBank rolesets into LiDARR to extend implicit roles in annotated AMR, allowing implicit roles to be linked through the coreference chains via AMRs.
%R 10.18653/v1/2025.acl-demo.41
%U https://aclanthology.org/2025.acl-demo.41/
%U https://doi.org/10.18653/v1/2025.acl-demo.41
%P 426-435
Markdown (Informal)
[LiDARR: Linking Document AMRs with Referents Resolvers](https://aclanthology.org/2025.acl-demo.41/) (Cai et al., ACL 2025)
ACL
- Jon Cai, Kristin Wright-Bettner, Zekun Zhao, Shafiuddin Rehan Ahmed, Abijith Trichur Ramachandran, Jeffrey Flanigan, Martha Palmer, and James Martin. 2025. LiDARR: Linking Document AMRs with Referents Resolvers. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 426–435, Vienna, Austria. Association for Computational Linguistics.