Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction

Jing Chen, Dechuan Yang, Xilian Li, Wei Chen, Tengjiao Wang


Abstract
This paper describes our participation in SemEval 2018 Task 2: Multilingual Emoji Prediction, in which participants are asked to predict a tweet’s most associated emoji from 20 emojis. Instead of regarding it as a 20-class classification problem we regard it as a text similarity problem. We propose a vector similarity based approach for this task. First the distributed representation (tweet vector) for each tweet is generated, then the similarity between this tweet vector and each emoji’s embedding is evaluated. The most similar emoji is chosen as the predicted label. Experimental results show that our approach performs comparably with the classification approach and shows its advantage in classifying emojis with similar semantic meaning.
Anthology ID:
S18-1067
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:
428–432
Language:
URL:
https://www.aclweb.org/anthology/S18-1067
DOI:
10.18653/v1/S18-1067
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1067.pdf