@inproceedings{tamari-etal-2021-process,
title = "Process-Level Representation of Scientific Protocols with Interactive Annotation",
author = "Tamari, Ronen and
Bai, Fan and
Ritter, Alan and
Stanovsky, Gabriel",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.187",
doi = "10.18653/v1/2021.eacl-main.187",
pages = "2190--2202",
abstract = "We develop Process Execution Graphs (PEG), a document-level representation of real-world wet lab biochemistry protocols, addressing challenges such as cross-sentence relations, long-range coreference, grounding, and implicit arguments. We manually annotate PEGs in a corpus of complex lab protocols with a novel interactive textual simulator that keeps track of entity traits and semantic constraints during annotation. We use this data to develop graph-prediction models, finding them to be good at entity identification and local relation extraction, while our corpus facilitates further exploration of challenging long-range relations.",
}
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%0 Conference Proceedings
%T Process-Level Representation of Scientific Protocols with Interactive Annotation
%A Tamari, Ronen
%A Bai, Fan
%A Ritter, Alan
%A Stanovsky, Gabriel
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F tamari-etal-2021-process
%X We develop Process Execution Graphs (PEG), a document-level representation of real-world wet lab biochemistry protocols, addressing challenges such as cross-sentence relations, long-range coreference, grounding, and implicit arguments. We manually annotate PEGs in a corpus of complex lab protocols with a novel interactive textual simulator that keeps track of entity traits and semantic constraints during annotation. We use this data to develop graph-prediction models, finding them to be good at entity identification and local relation extraction, while our corpus facilitates further exploration of challenging long-range relations.
%R 10.18653/v1/2021.eacl-main.187
%U https://aclanthology.org/2021.eacl-main.187
%U https://doi.org/10.18653/v1/2021.eacl-main.187
%P 2190-2202
Markdown (Informal)
[Process-Level Representation of Scientific Protocols with Interactive Annotation](https://aclanthology.org/2021.eacl-main.187) (Tamari et al., EACL 2021)
ACL