QCSE: 一种预训练的量子上下文敏感词嵌入用于自然语言处理
QCSE: A Pretrained Quantum Context-Sensitive Word Embedding for Natural Language Processing
AI总结：
QCSE是一种利用量子计算的上下文敏感词嵌入模型，通过创新的上下文矩阵计算方法提升自然语言处理的表达能力。
摘要：
量子自然语言处理（QNLP）通过量子计算的威力提供了一种新的方法，用于编码和理解自然语言的复杂性。本文提出了一种预训练的量子上下文敏感嵌入模型，称为QCSE，该模型能够捕捉上下文敏感的词嵌入，利用量子系统的独特性质来学习语言中的上下文关系。该模型引入了量子原生的上下文学习，使量子计算机能够用于语言任务。核心方法是创新的上下文矩阵计算方法，旨在根据周围的语言上下文创建独特的词表示。提出了五种不同的方法来计算上下文矩阵，包括指数衰减、正弦调制、相位变化和基于哈希的转换技术。这些方法确保量子嵌入保留上下文敏感性，从而使它们适合下游语言任务，其中量子系统的表达能力和特性成为宝贵资源。为了评估模型和相关上下文矩阵方法的有效性，评估在富拉语语料库（低资源非洲语言）和英语语料库上进行，前者是小规模数据集，后者稍大一些。结果表明，QCSE不仅捕捉了上下文敏感性，还利用了量子系统的表达能力来表示丰富的、上下文感知的语言信息。富拉语的使用进一步突显了QNLP在缓解此类语言数据不足问题方面的潜力。本工作强调了量子计算在自然语言处理（NLP）中的强大能力，并为将QNLP应用于各种任务和领域的实际语言挑战开辟了新途径。
英文摘要：
Quantum Natural Language Processing (QNLP) offers a novel approach to encoding and understanding the complexity of natural languages through the power of quantum computation. This paper presents a pretrained quantum context-sensitive embedding model, called QCSE, that captures context-sensitive word embeddings, leveraging the unique properties of quantum systems to learn contextual relationships in languages. The model introduces quantum-native context learning, enabling the utilization of quantum computers for linguistic tasks. Central to the proposed approach are innovative context matrix computation methods, designed to create unique, representations of words based on their surrounding linguistic context. Five distinct methods are proposed and tested for computing the context matrices, incorporating techniques such as exponential decay, sinusoidal modulation, phase shifts, and hash-based transformations. These methods ensure that the quantum embeddings retain context sensitivity, thereby making them suitable for downstream language tasks where the expressibility and properties of quantum systems are valuable resources. To evaluate the effectiveness of the model and the associated context matrix methods, evaluations are conducted on both a Fulani corpus, a low-resource African language, dataset of small size and an English corpus of slightly larger size. The results demonstrate that QCSE not only captures context sensitivity but also leverages the expressibility of quantum systems for representing rich, context-aware language information. The use of Fulani further highlights the potential of QNLP to mitigate the problem of lack of data for this category of languages. This work underscores the power of quantum computation in natural language processing (NLP) and opens new avenues for applying QNLP to real-world linguistic challenges across various tasks and domains.
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。
