论文标题
使用DiscoCat的多类Q-NLP情感分析实验
A multiclass Q-NLP sentiment analysis experiment using DisCoCat
论文作者
论文摘要
情感分析是自然语言处理（NLP）的分支，哪个目标是将情感或情感分配给特定的句子或单词。执行此任务对于希望通过聊天机器人或逐字了解客户反馈的公司特别有用。这是在文献中使用各种方法进行广泛完成的，从简单模型到深层变压器神经网络。在本文中，我们将使用语言模型在嘈杂的中级计算（NISQ）时代（NISQ）时代解决情感分析。我们将首先介绍量子计算的基础知识和DiscoCat模型。这将使我们能够定义一个通用框架，以在量子计算机上执行NLP任务。然后，我们将扩展Lorenz等人进行的两类分类。 （2021）到更大的数据集上的四类情绪分析实验，显示了这种框架的可扩展性。
Sentiment analysis is a branch of Natural Language Processing (NLP) which goal is to assign sentiments or emotions to particular sentences or words. Performing this task is particularly useful for companies wishing to take into account customer feedback through chatbots or verbatim. This has been done extensively in the literature using various approaches, ranging from simple models to deep transformer neural networks. In this paper, we will tackle sentiment analysis in the Noisy Intermediate Scale Computing (NISQ) era, using the DisCoCat model of language. We will first present the basics of quantum computing and the DisCoCat model. This will enable us to define a general framework to perform NLP tasks on a quantum computer. We will then extend the two-class classification that was performed by Lorenz et al. (2021) to a four-class sentiment analysis experiment on a much larger dataset, showing the scalability of such a framework.
