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
- Venues:
- SemEval | *SEM
- 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
- 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-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S18-1014.pdf