@inproceedings{suarez-paniagua-etal-2018-uc3m,
title = "{UC}3{M}-{NII} Team at {S}em{E}val-2018 Task 7: Semantic Relation Classification in Scientific Papers via Convolutional Neural Network",
author = "Su{\'a}rez-Paniagua, V{\'\i}ctor and
Segura-Bedmar, Isabel and
Aizawa, Akiko",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1126",
doi = "10.18653/v1/S18-1126",
pages = "793--797",
abstract = "This paper reports our participation for SemEval-2018 Task 7 on extraction and classification of relationships between entities in scientific papers. Our approach is based on the use of a Convolutional Neural Network (CNN) trained on350 abstract with manually annotated entities and relations. Our hypothesis is that this deep learning model can be applied to extract and classify relations between entities for scientific papers at the same time. We use the Part-of-Speech and the distances to the target entities as part of the embedding for each word and we blind all the entities by marker names. In addition, we use sampling techniques to overcome the imbalance issues of this dataset. Our architecture obtained an F1-score of 35.4{\%} for the relation extraction task and 18.5{\%} for the relation classification task with a basic configuration of the one step CNN.",
}
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<abstract>This paper reports our participation for SemEval-2018 Task 7 on extraction and classification of relationships between entities in scientific papers. Our approach is based on the use of a Convolutional Neural Network (CNN) trained on350 abstract with manually annotated entities and relations. Our hypothesis is that this deep learning model can be applied to extract and classify relations between entities for scientific papers at the same time. We use the Part-of-Speech and the distances to the target entities as part of the embedding for each word and we blind all the entities by marker names. In addition, we use sampling techniques to overcome the imbalance issues of this dataset. Our architecture obtained an F1-score of 35.4% for the relation extraction task and 18.5% for the relation classification task with a basic configuration of the one step CNN.</abstract>
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%0 Conference Proceedings
%T UC3M-NII Team at SemEval-2018 Task 7: Semantic Relation Classification in Scientific Papers via Convolutional Neural Network
%A Suárez-Paniagua, Víctor
%A Segura-Bedmar, Isabel
%A Aizawa, Akiko
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F suarez-paniagua-etal-2018-uc3m
%X This paper reports our participation for SemEval-2018 Task 7 on extraction and classification of relationships between entities in scientific papers. Our approach is based on the use of a Convolutional Neural Network (CNN) trained on350 abstract with manually annotated entities and relations. Our hypothesis is that this deep learning model can be applied to extract and classify relations between entities for scientific papers at the same time. We use the Part-of-Speech and the distances to the target entities as part of the embedding for each word and we blind all the entities by marker names. In addition, we use sampling techniques to overcome the imbalance issues of this dataset. Our architecture obtained an F1-score of 35.4% for the relation extraction task and 18.5% for the relation classification task with a basic configuration of the one step CNN.
%R 10.18653/v1/S18-1126
%U https://aclanthology.org/S18-1126
%U https://doi.org/10.18653/v1/S18-1126
%P 793-797
Markdown (Informal)
[UC3M-NII Team at SemEval-2018 Task 7: Semantic Relation Classification in Scientific Papers via Convolutional Neural Network](https://aclanthology.org/S18-1126) (Suárez-Paniagua et al., SemEval 2018)
ACL