MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network

Di Jin, Franck Dernoncourt, Elena Sergeeva, Matthew McDermott, Geeticka Chauhan


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.
Anthology ID:
S18-1127
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
798–804
Language:
URL:
https://www.aclweb.org/anthology/S18-1127
DOI:
10.18653/v1/S18-1127
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1127.pdf