Mattias Nilsson


2018

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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
Proceedings of the 12th International Workshop on Semantic Evaluation

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.

2012

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Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics
Pierre Lison | Mattias Nilsson | Marta Recasens
Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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A Survival Analysis of Fixation Times in Reading
Mattias Nilsson | Joakim Nivre
Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics

2010

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Towards a Data-Driven Model of Eye Movement Control in Reading
Mattias Nilsson | Joakim Nivre
Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics

2009

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Learning Where to Look: Modeling Eye Movements in Reading
Mattias Nilsson | Joakim Nivre
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

2007

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Single Malt or Blended? A Study in Multilingual Parser Optimization
Johan Hall | Jens Nilsson | Joakim Nivre | Gülşen Eryiǧit | Beáta Megyesi | Mattias Nilsson | Markus Saers
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)