Abstract
This paper describes the participation of USI-IR in WASSA 2018 Implicit Emotion Shared Task. We propose a relevance feedback approach employing a sequential model (biLSTM) and word embeddings derived from a large collection of tweets. To this end, we assume that the top-k predictions produce at a first classification step are correct (based on the model accuracy) and use them as new examples to re-train the network.- Anthology ID:
- W18-6233
- Volume:
- Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Alexandra Balahur, Saif M. Mohammad, Veronique Hoste, Roman Klinger
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 231–234
- Language:
- URL:
- https://aclanthology.org/W18-6233
- DOI:
- 10.18653/v1/W18-6233
- Cite (ACL):
- Esteban Ríssola, Anastasia Giachanou, and Fabio Crestani. 2018. USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 231–234, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection (Ríssola et al., WASSA 2018)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/W18-6233.pdf