@inproceedings{ma-etal-2022-joint,
title = "Joint Entity and Relation Extraction Based on Table Labeling Using Convolutional Neural Networks",
author = "Ma, Youmi and
Hiraoka, Tatsuya and
Okazaki, Naoaki",
editor = "Vlachos, Andreas and
Agrawal, Priyanka and
Martins, Andr{\'e} and
Lampouras, Gerasimos and
Lyu, Chunchuan",
booktitle = "Proceedings of the Sixth Workshop on Structured Prediction for NLP",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.spnlp-1.2",
doi = "10.18653/v1/2022.spnlp-1.2",
pages = "11--21",
abstract = "This study introduces a novel approach to the joint extraction of entities and relations by stacking convolutional neural networks (CNNs) on pretrained language models. We adopt table representations to model the entities and relations, casting the entity and relation extraction as a table-labeling problem. Regarding each table as an image and each cell in a table as an image pixel, we apply two-dimensional CNNs to the tables to capture local dependencies and predict the cell labels. The experimental results showed that the performance of the proposed method is comparable to those of current state-of-art systems on the CoNLL04, ACE05, and ADE datasets. Even when freezing pretrained language model parameters, the proposed method showed a stable performance, whereas the compared methods suffered from significant decreases in performance. This observation indicates that the parameters of the pretrained encoder may incorporate dependencies among the entity and relation labels during fine-tuning.",
}
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<abstract>This study introduces a novel approach to the joint extraction of entities and relations by stacking convolutional neural networks (CNNs) on pretrained language models. We adopt table representations to model the entities and relations, casting the entity and relation extraction as a table-labeling problem. Regarding each table as an image and each cell in a table as an image pixel, we apply two-dimensional CNNs to the tables to capture local dependencies and predict the cell labels. The experimental results showed that the performance of the proposed method is comparable to those of current state-of-art systems on the CoNLL04, ACE05, and ADE datasets. Even when freezing pretrained language model parameters, the proposed method showed a stable performance, whereas the compared methods suffered from significant decreases in performance. This observation indicates that the parameters of the pretrained encoder may incorporate dependencies among the entity and relation labels during fine-tuning.</abstract>
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%0 Conference Proceedings
%T Joint Entity and Relation Extraction Based on Table Labeling Using Convolutional Neural Networks
%A Ma, Youmi
%A Hiraoka, Tatsuya
%A Okazaki, Naoaki
%Y Vlachos, Andreas
%Y Agrawal, Priyanka
%Y Martins, André
%Y Lampouras, Gerasimos
%Y Lyu, Chunchuan
%S Proceedings of the Sixth Workshop on Structured Prediction for NLP
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ma-etal-2022-joint
%X This study introduces a novel approach to the joint extraction of entities and relations by stacking convolutional neural networks (CNNs) on pretrained language models. We adopt table representations to model the entities and relations, casting the entity and relation extraction as a table-labeling problem. Regarding each table as an image and each cell in a table as an image pixel, we apply two-dimensional CNNs to the tables to capture local dependencies and predict the cell labels. The experimental results showed that the performance of the proposed method is comparable to those of current state-of-art systems on the CoNLL04, ACE05, and ADE datasets. Even when freezing pretrained language model parameters, the proposed method showed a stable performance, whereas the compared methods suffered from significant decreases in performance. This observation indicates that the parameters of the pretrained encoder may incorporate dependencies among the entity and relation labels during fine-tuning.
%R 10.18653/v1/2022.spnlp-1.2
%U https://aclanthology.org/2022.spnlp-1.2
%U https://doi.org/10.18653/v1/2022.spnlp-1.2
%P 11-21
Markdown (Informal)
[Joint Entity and Relation Extraction Based on Table Labeling Using Convolutional Neural Networks](https://aclanthology.org/2022.spnlp-1.2) (Ma et al., spnlp 2022)
ACL