@inproceedings{san-2018-random,
title = "Random Decision Syntax Trees at {S}em{E}val-2018 Task 3: {LSTM}s and Sentiment Scores for Irony Detection",
author = "San, Aidan",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1091",
doi = "10.18653/v1/S18-1091",
pages = "560--564",
abstract = "We propose a Long Short Term Memory Neural Network model for irony detection in tweets in this paper. Our model is trained using word embeddings and emoji embeddings. We show that adding sentiment scores to our model improves the F1 score of our baseline LSTM by approximately .012, and therefore show that high-level features can be used to improve word embeddings in certain Natural Language Processing applications. Our model ranks 24/43 for binary classification and 5/31 for multiclass classification. We make our model easily accessible to the research community.",
}
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%0 Conference Proceedings
%T Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection
%A San, Aidan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F san-2018-random
%X We propose a Long Short Term Memory Neural Network model for irony detection in tweets in this paper. Our model is trained using word embeddings and emoji embeddings. We show that adding sentiment scores to our model improves the F1 score of our baseline LSTM by approximately .012, and therefore show that high-level features can be used to improve word embeddings in certain Natural Language Processing applications. Our model ranks 24/43 for binary classification and 5/31 for multiclass classification. We make our model easily accessible to the research community.
%R 10.18653/v1/S18-1091
%U https://aclanthology.org/S18-1091
%U https://doi.org/10.18653/v1/S18-1091
%P 560-564
Markdown (Informal)
[Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection](https://aclanthology.org/S18-1091) (San, SemEval 2018)
ACL