使用DisCoCat的多类Q-NLP情感分析实验
A multiclass Q-NLP sentiment analysis experiment using DisCoCat
摘要：
情感分析是自然语言处理（NLP）的一个分支，其目标是将情感或情绪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记录来考虑客户反馈的公司特别有用。这在文献中已经使用各种方法广泛地完成，从简单模型到深度Transformer神经网络。在本文中，我们将解决情感分析在噪声中间规模计算（NISQ）时代，使用语言的DiscoCat模型。我们将首先介绍量子计算和DisCoCat模型的基础知识。这将使我们能够定义一个通用框架来在量子计算机上执行NLP任务。然后，我们将Lorenz等人（2021）执行的两类分类扩展到更大数据集上的四类情感分析实验，显示了这种框架的可扩展性。
英文摘要：
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.
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。
