{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ntuer-at-semeval-2019-task-3-emotion","title":"ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN","arxiv_id":"1902.07867","date":"2019-02-21","proceeding":"SEMEVAL 2019 6","authors":["Peixiang Zhong","Chunyan Miao"],"abstract":"In this paper we present our model on the task of emotion detection in\ntextual conversations in SemEval-2019. Our model extends the Recurrent\nConvolutional Neural Network (RCNN) by using external fine-tuned word\nrepresentations and DeepMoji sentence representations. We also explored several\nother competitive pre-trained word and sentence representations including ELMo,\nBERT and InferSent but found inferior performance. In addition, we conducted\nextensive sensitivity analysis, which empirically shows that our model is\nrelatively robust to hyper-parameters. Our model requires no handcrafted\nfeatures or emotion lexicons but achieved good performance with a micro-F1\nscore of 0.7463.","url_abs":"http://arxiv.org/abs/1902.07867v2","url_pdf":"http://arxiv.org/pdf/1902.07867v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ntuer-at-semeval-2019-task-3-emotion","repo_url":"https://github.com/zhongpeixiang/SemEval2019-Task3-EmotionDetection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sensitivity","task_name":"Sensitivity"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[{"method_slug":"bilstm","method_name":"BiLSTM"},{"method_slug":"elmo","method_name":"ELMo"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}