LDEB——用于会话对话情感识别的情感二值化与机器学习标签数字化方法
LDEB -- Label Digitization with Emotion Binarization and Machine Learning for Emotion Recognition in Conversational Dialogues
AI总结：
针对会话对话中嵌套情感导致标签关联纠缠及数据稀缺问题，提出LDEB方法，结合文本规范化、7位编码及RF与ANN分层模型，在FETA-DailyDialog上实现最高约78%准确率。
摘要：
对话中的情感识别（ERC）对会话式人工智能及其应用的发展至关重要。因此，利用机器学习（ML）概念开发自动化ERC模型将是有益的。然而，会话对话呈现出一个独特的问题：每段对话都描绘了嵌套的情感，这些情感使情感特征描述符与情感类型（或标签）之间的关联纠缠在一起。这种纠缠会因数据稀缺的存在而加剧，成为ML模型面临的障碍。为克服这一问题，我们提出了一种名为“情感二值化标签数字化”（LDEB）的新颖方法，该方法利用文本规范化和7位数字编码技术来解开这些扭结，并为待训练的ML模型构建有意义的特征空间。我们还利用名为FETA-DailyDialog的公开数据集进行特征学习，并使用随机森林（RF）和人工神经网络（ANN）分类器开发了分层ERC模型。仿真表明，基于ANN的ERC模型能够以分别约74%和76%的最佳准确率和精确率预测情感。仿真还表明，ANN模型在60个epoch时可达到约98%的训练准确率。另一方面，基于RF的ERC模型能够以分别约78%和75%的最佳准确率和精确率预测情感。
英文摘要：
Emotion recognition in conversations (ERC) is vital to the advancements of conversational AI and its applications. Therefore, the development of an automated ERC model using the concepts of machine learning (ML) would be beneficial. However, the conversational dialogues present a unique problem where each dialogue depicts nested emotions that entangle the association between the emotional feature descriptors and emotion type (or label). This entanglement that can be multiplied with the presence of data paucity is an obstacle for a ML model. To overcome this problem, we proposed a novel approach called Label Digitization with Emotion Binarization (LDEB) that disentangles the twists by utilizing the text normalization and 7-bit digital encoding techniques and constructs a meaningful feature space for a ML model to be trained. We also utilized the publicly available dataset called the FETA-DailyDialog dataset for feature learning and developed a hierarchical ERC model using random forest (RF) and artificial neural network (ANN) classifiers. Simulations showed that the ANN-based ERC model was able to predict emotion with the best accuracy and precision scores of about 74% and 76%, respectively. Simulations also showed that the ANN-model could reach a training accuracy score of about 98% with 60 epochs. On the other hand, the RF-based ERC model was able to predict emotions with the best accuracy and precision scores of about 78% and 75%, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
