人工智能驱动的西班牙语诗歌分类框架

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shutian Deng, Gang Wang, Hongjun Wang, Fuliang Chang
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引用次数: 0

摘要

西班牙拥有大量的诗歌。大多数诗歌的特点是风格迥异。由于这些诗歌的复杂性,肤浅的阅读可能会使读者感到困惑。因此,提前对诗歌风格进行分类至关重要。目前,诗歌分类研究多以人工方式进行,这对分类者的专业素质提出了极高的要求,也耗费了大量时间。此外,由于分类器的主观性影响,分类的客观性也无法保证。为了解决这些问题,利用人工智能技术设计了西班牙诗歌分类框架,提高了分类的准确性、效率和客观性。首先,详细介绍了人工智能驱动的西班牙语诗歌分类框架,并通过框架图清晰地表示了分类过程中的每一个步骤。该框架包括多种算法和模型,如词频-反向文档频率(TF_IDF)、袋式分类、支持向量机(SVM)、自适应提升(AdaBoost)、逻辑回归(LR)、梯度提升决策树(GBDT)、LightGBM(LGB)、极端梯度提升(XGBoost)和随机森林(RF)。每种算法在框架中的作用都有明确定义。最后,对模型选择进行了实验,比较了这些算法的结果。Bagging 模型因其高精度而脱颖而出,实验结果表明,所提出的框架可以帮助研究人员更高效、准确、客观地开展诗歌研究工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Artificial-Intelligence-Driven Spanish Poetry Classification Framework
Spain possesses a vast number of poems. Most have features that mean they present significantly different styles. A superficial reading of these poems may confuse readers due to their complexity. Therefore, it is of vital importance to classify the style of the poems in advance. Currently, poetry classification studies are mostly carried out manually, which creates extremely high requirements for the professional quality of classifiers and consumes a large amount of time. Furthermore, the objectivity of the classification cannot be guaranteed because of the influence of the classifier’s subjectivity. To solve these problems, a Spanish poetry classification framework was designed using artificial intelligence technology, which improves the accuracy, efficiency, and objectivity of classification. First, an artificial-intelligence-driven Spanish poetry classification framework is described in detail, and is illustrated by a framework diagram to clearly represent each step in the process. The framework includes many algorithms and models, such as the Term Frequency–Inverse Document Frequency (TF_IDF), Bagging, Support Vector Machines (SVMs), Adaptive Boosting (AdaBoost), logistic regression (LR), Gradient Boosting Decision Trees (GBDT), LightGBM (LGB), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The roles of each algorithm in the framework are clearly defined. Finally, experiments were performed for model selection, comparing the results of these algorithms.The Bagging model stood out for its high accuracy, and the experimental results showed that the proposed framework can help researchers carry out poetry research work more efficiently, accurately, and objectively.
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来源期刊
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
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