FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis

Maja Karasalo, Mattias Nilsson, Magnus Rosell, Ulrika Wickenberg Bolin


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.
Anthology ID:
S18-1014
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:
109–115
Language:
URL:
https://aclanthology.org/S18-1014
DOI:
10.18653/v1/S18-1014
Bibkey:
Cite (ACL):
Maja Karasalo, Mattias Nilsson, Magnus Rosell, and Ulrika Wickenberg Bolin. 2018. FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis. In Proceedings of The 12th International Workshop on Semantic Evaluation, pages 109–115, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis (Karasalo et al., SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1014.pdf