AI 中文总结
针对印地语在量子自然语言处理中受关注少且语言复杂的情况，提出语法感知QNLP管道用于印地语情感分类，利用预群语法编码句子，经Lambeq生成量子电路，用HQNNs训练，实现有效情感分类，凸显组合QNLP对形态丰富语言的潜力。
AI 中文摘要
自然语言处理（NLP）在经典和量子平台上的进展主要集中在英语上，因其广泛使用和丰富的语言资源。印地语作为世界第三大语言，在计算语言学中受到的关注相对有限。它与英语在文字、句法结构和语言特征上有显著差异。我们提出了一种用于印地语情感分类的语法感知量子自然语言处理（QNLP）管道，重点关注句子否定。使用手动标注的印地语情感数据集，通过预群语法类型对句子进行编码，并用Lambeq处理句子以生成量子电路。训练混合量子神经网络（HQNNs）进行二元和三元情感分类。结果证明了有效的情感分类，并突出了组合QNLP对形态丰富语言的潜力。
英文摘要
Advancements in Natural Language Processing (NLP), whether on classical or quantum platforms, have predominantly focused on English due to its widespread use and abundant linguistic resources. Although English remains the most studied language in computational linguistics, Hindi, the third most spoken language worldwide after Mandarin, has received comparatively limited attention. Spoken primarily in India, Hindi differs significantly from English in its script, syntactic structure, and linguistic characteristics. Hindi uses the Devanagari script, exhibits rich morphological inflection, and follows a subject-object-verb (SOV) word order, unlike English's subject-verb-object (SVO) structure. Motivated by Hindi's linguistic complexity and its underrepresentation in Quantum Natural Language Processing (QNLP), we propose a grammar-aware QNLP pipeline for Hindi sentiment classification with a focus on sentential negation. We use a manually annotated Hindi sentiment dataset labeled as positive, negative, or neutral, and encode sentences using pregroup grammar types. Sentences are processed with Lambeq to generate quantum circuits using a novel negation-aware compositional grammar. Hybrid Quantum Neural Networks (HQNNs) are trained for both binary and ternary sentiment classification. Our results demonstrate effective sentiment classification and highlight the potential of compositional QNLP for morphologically rich languages.
Comments10 pages, 6 figures
