Abstract
This paper describes the system developed by the Centre for English Corpus Linguistics for the SemEval-2019 Task 3: EmoContext. It aimed at classifying the emotion of a user utterance in a textual conversation as happy, sad, angry or other. It is based on a large number of feature types, mainly unigrams and bigrams, which were extracted by a SAS program. The usefulness of the different feature types was evaluated by means of Monte-Carlo resampling tests. As this system does not rest on any deep learning component, which is currently considered as the state-of-the-art approach, it can be seen as a possible point of comparison for such kind of systems.- Anthology ID:
- S19-2022
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 148–152
- Language:
- URL:
- https://aclanthology.org/S19-2022
- DOI:
- 10.18653/v1/S19-2022
- Cite (ACL):
- Yves Bestgen. 2019. CECL at SemEval-2019 Task 3: Using Surface Learning for Detecting Emotion in Textual Conversations. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 148–152, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- CECL at SemEval-2019 Task 3: Using Surface Learning for Detecting Emotion in Textual Conversations (Bestgen, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S19-2022.pdf
- Data
- EmoContext