@inproceedings{jin-etal-2018-mit,
title = "{MIT}-{MEDG} at {S}em{E}val-2018 Task 7: Semantic Relation Classification via Convolution Neural Network",
author = "Jin, Di and
Dernoncourt, Franck and
Sergeeva, Elena and
McDermott, Matthew and
Chauhan, Geeticka",
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/S18-1127",
doi = "10.18653/v1/S18-1127",
pages = "798--804",
abstract = "SemEval 2018 Task 7 tasked participants to build a system to classify two entities within a sentence into one of the 6 possible relation types. We tested 3 classes of models: Linear classifiers, Long Short-Term Memory (LSTM) models, and Convolutional Neural Network (CNN) models. Ultimately, the CNN model class proved most performant, so we specialized to this model for our final submissions. We improved performance beyond a vanilla CNN by including a variant of negative sampling, using custom word embeddings learned over a corpus of ACL articles, training over corpora of both tasks 1.1 and 1.2, using reversed feature, using part of context words beyond the entity pairs and using ensemble methods to improve our final predictions. We also tested attention based pooling, up-sampling, and data augmentation, but none improved performance. Our model achieved rank 6 out of 28 (macro-averaged F1-score: 72.7) in subtask 1.1, and rank 4 out of 20 (macro F1: 80.6) in subtask 1.2.",
}
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<abstract>SemEval 2018 Task 7 tasked participants to build a system to classify two entities within a sentence into one of the 6 possible relation types. We tested 3 classes of models: Linear classifiers, Long Short-Term Memory (LSTM) models, and Convolutional Neural Network (CNN) models. Ultimately, the CNN model class proved most performant, so we specialized to this model for our final submissions. We improved performance beyond a vanilla CNN by including a variant of negative sampling, using custom word embeddings learned over a corpus of ACL articles, training over corpora of both tasks 1.1 and 1.2, using reversed feature, using part of context words beyond the entity pairs and using ensemble methods to improve our final predictions. We also tested attention based pooling, up-sampling, and data augmentation, but none improved performance. Our model achieved rank 6 out of 28 (macro-averaged F1-score: 72.7) in subtask 1.1, and rank 4 out of 20 (macro F1: 80.6) in subtask 1.2.</abstract>
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%0 Conference Proceedings
%T MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network
%A Jin, Di
%A Dernoncourt, Franck
%A Sergeeva, Elena
%A McDermott, Matthew
%A Chauhan, Geeticka
%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 jin-etal-2018-mit
%X SemEval 2018 Task 7 tasked participants to build a system to classify two entities within a sentence into one of the 6 possible relation types. We tested 3 classes of models: Linear classifiers, Long Short-Term Memory (LSTM) models, and Convolutional Neural Network (CNN) models. Ultimately, the CNN model class proved most performant, so we specialized to this model for our final submissions. We improved performance beyond a vanilla CNN by including a variant of negative sampling, using custom word embeddings learned over a corpus of ACL articles, training over corpora of both tasks 1.1 and 1.2, using reversed feature, using part of context words beyond the entity pairs and using ensemble methods to improve our final predictions. We also tested attention based pooling, up-sampling, and data augmentation, but none improved performance. Our model achieved rank 6 out of 28 (macro-averaged F1-score: 72.7) in subtask 1.1, and rank 4 out of 20 (macro F1: 80.6) in subtask 1.2.
%R 10.18653/v1/S18-1127
%U https://aclanthology.org/S18-1127
%U https://doi.org/10.18653/v1/S18-1127
%P 798-804
Markdown (Informal)
[MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network](https://aclanthology.org/S18-1127) (Jin et al., SemEval 2018)
ACL