EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM

Man Liu


Abstract
This paper describes our system used in the English Emoji Prediction Task 2 at the SemEval-2018. Our system is based on two supervised machine learning algorithms: Gradient Boosting Regression Tree Method (GBM) and Bidirectional Long Short-term Memory Network (BLSTM). Besides the common features, we extract various lexicon and syntactic features from external resources. After comparing the results of two algorithms, GBM is chosen for the final evaluation.
Anthology ID:
S18-1059
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:
390–394
Language:
URL:
https://www.aclweb.org/anthology/S18-1059
DOI:
10.18653/v1/S18-1059
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1059.pdf