@inproceedings{karasalo-etal-2018-foi,
title = "{FOI} {DSS} at {S}em{E}val-2018 Task 1: Combining {LSTM} States, Embeddings, and Lexical Features for Affect Analysis",
author = "Karasalo, Maja and
Nilsson, Mattias and
Rosell, Magnus and
Wickenberg Bolin, Ulrika",
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/S18-1014",
doi = "10.18653/v1/S18-1014",
pages = "109--115",
abstract = "This paper describes the system used and results obtained for team FOI DSS at SemEval-2018 Task 1: Affect In Tweets. The team participated in all English language subtasks, with a method utilizing transfer learning from LSTM nets trained on large sentiment datasets combined with embeddings and lexical features. For four out of five subtasks, the system performed in the range of 92-95{\%} of the winning systems, in terms of the competition metrics. Analysis of the results suggests that improved pre-processing and addition of more lexical features may further elevate performance.",
}
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<abstract>This paper describes the system used and results obtained for team FOI DSS at SemEval-2018 Task 1: Affect In Tweets. The team participated in all English language subtasks, with a method utilizing transfer learning from LSTM nets trained on large sentiment datasets combined with embeddings and lexical features. For four out of five subtasks, the system performed in the range of 92-95% of the winning systems, in terms of the competition metrics. Analysis of the results suggests that improved pre-processing and addition of more lexical features may further elevate performance.</abstract>
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%0 Conference Proceedings
%T FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis
%A Karasalo, Maja
%A Nilsson, Mattias
%A Rosell, Magnus
%A Wickenberg Bolin, Ulrika
%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 karasalo-etal-2018-foi
%X This paper describes the system used and results obtained for team FOI DSS at SemEval-2018 Task 1: Affect In Tweets. The team participated in all English language subtasks, with a method utilizing transfer learning from LSTM nets trained on large sentiment datasets combined with embeddings and lexical features. For four out of five subtasks, the system performed in the range of 92-95% of the winning systems, in terms of the competition metrics. Analysis of the results suggests that improved pre-processing and addition of more lexical features may further elevate performance.
%R 10.18653/v1/S18-1014
%U https://aclanthology.org/S18-1014
%U https://doi.org/10.18653/v1/S18-1014
%P 109-115
Markdown (Informal)
[FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis](https://aclanthology.org/S18-1014) (Karasalo et al., SemEval 2018)
ACL