Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks

Bhanu Pratap, Daniel Shank, Oladipo Ositelu, Byron Galbraith


Abstract
This paper describes our approach to SemEval-2018 Task 7 – given an entity-tagged text from the ACL Anthology corpus, identify and classify pairs of entities that have one of six possible semantic relationships. Our model consists of a convolutional neural network leveraging pre-trained word embeddings, unlabeled ACL-abstracts, and multiple window sizes to automatically learn useful features from entity-tagged sentences. We also experiment with a hybrid loss function, a combination of cross-entropy loss and ranking loss, to boost the separation in classification scores. Lastly, we include WordNet-based features to further improve the performance of our model. Our best model achieves an F1(macro) score of 74.2 and 84.8 on subtasks 1.1 and 1.2, respectively.
Anthology ID:
S18-1139
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:
863–867
Language:
URL:
https://www.aclweb.org/anthology/S18-1139
DOI:
10.18653/v1/S18-1139
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1139.pdf