FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis
| Autor: | Magnus Rosell, Maja Karasalo, Ulrika Wickenberg Bolin, Mattias Nilsson |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry 02 engineering and technology computer.software_genre SemEval Task (project management) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Affect (linguistics) Artificial intelligence Transfer of learning business computer Natural language processing |
| Zdroj: | SemEval@NAACL-HLT |
| DOI: | 10.18653/v1/s18-1014 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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