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
- Venues:
- SemEval | *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 863–867
- Language:
- URL:
- https://aclanthology.org/S18-1139
- DOI:
- 10.18653/v1/S18-1139
- Cite (ACL):
- Bhanu Pratap, Daniel Shank, Oladipo Ositelu, and Byron Galbraith. 2018. Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 863–867, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks (Pratap et al., SemEval-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S18-1139.pdf