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首页> 外文会议>IEEE International Conference on Big Data and Smart Computing >Word-Level Emotion Embedding Based on Semi-Supervised Learning for Emotional Classification in Dialogue
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Word-Level Emotion Embedding Based on Semi-Supervised Learning for Emotional Classification in Dialogue

机译：基于半监督学习的词级情感嵌入在对话中的情感分类

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摘要

Emotion classification has been remarkable studies in recent years. However, most of works do not consider the context information such as a flow of emotions. In this paper, we propose the emotion classification in dialogue based on the semi-supervised word-level emotion embedding. For the word-level emotion embedding, we use the NRC Emotion Lexicon which is a list of English words and their associations with eight basic emotions. By adding word-level emotion vectors, we obtain an utterance-level emotion vector. We train a single layer LSTM-based classification network in dialogue. Also, we will evaluate our model on the EmotionLines which is dataset with emotions labeling on all utterances in each dialogue. The experiment plan is described in this paper.
机译：近年来，情绪分类一直是非凡的研究。但是，大多数作品都没有考虑情境信息，例如情感流。在本文中，我们提出了基于半监督词级情感嵌入的对话中情感分类。对于单词级情感嵌入，我们使用NRC Emotion词典，它是英语单词及其与八种基本情感的关联的列表。通过添加单词级情感矢量，我们获得了话语级情感矢量。我们在对话中训练了一个基于LSTM的单层分类网络。同样，我们将在EmotionLines上评估模型，EmotionLines是在每个对话的所有话语上都带有情感标签的数据集。本文描述了实验计划。

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