Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/106025
Title: | ntuer at SemEval-2019 Task 3 : Emotion classification with word and sentence representations in RCNN | Authors: | Zhong, Peixiang Miao, Chunyan |
Keywords: | Emotions Data Preprocessing Engineering::Computer science and engineering |
Issue Date: | 2019 | Source: | Zhong, P., & Miao, C. (2019). ntuer at SemEval-2019 Task 3 : Emotion classification with word and sentence representations in RCNN. Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2016). | Conference: | Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2016) | Abstract: | In this paper we present our model on the task of emotion detection in textual conversations in SemEval-2019. Our model extends the Recurrent Convolutional Neural Network (RCNN) by using external fine-tuned word representations and DeepMoji sentence representations. We also explored several other competitive pre-trained word and sentence representations including ELMo, BERT and InferSent but found inferior performance. In addition, we conducted extensive sensitivity analysis, which empirically shows that our model is relatively robust to hyper-parameters. Our model requires no handcrafted features or emotion lexicons but achieved good performance with a micro-F1 score of 0.7463. | URI: | https://hdl.handle.net/10356/106025 http://hdl.handle.net/10220/49236 |
Schools: | School of Computer Science and Engineering | Rights: | © 2019 Association for Computational Linguistics (ACL). All rights reserved. This paper was published in Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2016) and is made available with permission of Association for Computational Linguistics (ACL). | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Conference Papers |
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ntuer at SemEval-2019 Task 3 Emotion Classification with Word and Sentence Representations in RCNN (2).pdf | 162.56 kB | Adobe PDF | View/Open |
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