@inproceedings{sohrab-etal-2020-mgsohrab,
title = "mgsohrab at {WNUT} 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols",
author = "Sohrab, Mohammad Golam and
Duong Nguyen, Anh-Khoa and
Miwa, Makoto and
Takamura, Hiroya",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wnut-1.38",
doi = "10.18653/v1/2020.wnut-1.38",
pages = "290--298",
abstract = "We present a neural exhaustive approach that addresses named entity recognition (NER) and relation recognition (RE), for the entity and re- lation recognition over the wet-lab protocols shared task. We introduce BERT-based neural exhaustive approach that enumerates all pos- sible spans as potential entity mentions and classifies them into entity types or no entity with deep neural networks to address NER. To solve relation extraction task, based on the NER predictions or given gold mentions we create all possible trigger-argument pairs and classify them into relation types or no relation. In NER task, we achieved 76.60{\%} in terms of F-score as third rank system among the partic- ipated systems. In relation extraction task, we achieved 80.46{\%} in terms of F-score as the top system in the relation extraction or recognition task. Besides we compare our model based on the wet lab protocols corpus (WLPC) with the WLPC baseline and dynamic graph-based in- formation extraction (DyGIE) systems.",
}
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<abstract>We present a neural exhaustive approach that addresses named entity recognition (NER) and relation recognition (RE), for the entity and re- lation recognition over the wet-lab protocols shared task. We introduce BERT-based neural exhaustive approach that enumerates all pos- sible spans as potential entity mentions and classifies them into entity types or no entity with deep neural networks to address NER. To solve relation extraction task, based on the NER predictions or given gold mentions we create all possible trigger-argument pairs and classify them into relation types or no relation. In NER task, we achieved 76.60% in terms of F-score as third rank system among the partic- ipated systems. In relation extraction task, we achieved 80.46% in terms of F-score as the top system in the relation extraction or recognition task. Besides we compare our model based on the wet lab protocols corpus (WLPC) with the WLPC baseline and dynamic graph-based in- formation extraction (DyGIE) systems.</abstract>
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%0 Conference Proceedings
%T mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols
%A Sohrab, Mohammad Golam
%A Duong Nguyen, Anh-Khoa
%A Miwa, Makoto
%A Takamura, Hiroya
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sohrab-etal-2020-mgsohrab
%X We present a neural exhaustive approach that addresses named entity recognition (NER) and relation recognition (RE), for the entity and re- lation recognition over the wet-lab protocols shared task. We introduce BERT-based neural exhaustive approach that enumerates all pos- sible spans as potential entity mentions and classifies them into entity types or no entity with deep neural networks to address NER. To solve relation extraction task, based on the NER predictions or given gold mentions we create all possible trigger-argument pairs and classify them into relation types or no relation. In NER task, we achieved 76.60% in terms of F-score as third rank system among the partic- ipated systems. In relation extraction task, we achieved 80.46% in terms of F-score as the top system in the relation extraction or recognition task. Besides we compare our model based on the wet lab protocols corpus (WLPC) with the WLPC baseline and dynamic graph-based in- formation extraction (DyGIE) systems.
%R 10.18653/v1/2020.wnut-1.38
%U https://aclanthology.org/2020.wnut-1.38
%U https://doi.org/10.18653/v1/2020.wnut-1.38
%P 290-298
Markdown (Informal)
[mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols](https://aclanthology.org/2020.wnut-1.38) (Sohrab et al., WNUT 2020)
ACL