Abstract
In this paper, we present SyntNN as a way to include traditional syntactic models in multilayer neural networks used in the task of Semeval Task 2 of emoji prediction. The model builds on the distributed tree embedder also known as distributed tree kernel. Initial results are extremely encouraging but additional analysis is needed to overcome the problem of overfitting.- Anthology ID:
- S18-1076
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venues:
- SemEval | *SEM
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 477–481
- Language:
- URL:
- https://aclanthology.org/S18-1076
- DOI:
- 10.18653/v1/S18-1076
- Cite (ACL):
- Fabio Massimo Zanzotto and Andrea Santilli. 2018. SyntNN at SemEval-2018 Task 2: is Syntax Useful for Emoji Prediction? Embedding Syntactic Trees in Multi Layer Perceptrons. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 477–481, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- SyntNN at SemEval-2018 Task 2: is Syntax Useful for Emoji Prediction? Embedding Syntactic Trees in Multi Layer Perceptrons (Zanzotto & Santilli, SemEval-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/S18-1076.pdf