评估BERT与ParsBERT在波斯语广告数据分析中的表现
evaluating bert and parsbert for analyzing persian advertisement data
AI总结：
本研究以伊朗在线平台Divar的波斯语汽车广告数据集为对象，使用Hazm库处理文本，对比mBERT与ParsBERT的性能，梳理数据挖掘流程与机器学习相关方法，为低资源语言文本分析提供参考。
摘要：
本文探讨了互联网对现代贸易的影响，以及这些交易产生的数据对组织优化营销工作的重要性。论文以伊朗的产品与服务在线交易平台Divar为例，介绍了一项预测汽车销售广告在Divar网站上发布占比的竞赛。由于该数据集提供了丰富的波斯语文本数据资源，作者使用专为波斯语文本处理设计的Python库Hazm，以及两种最先进的语言模型mBERT和ParsBERT对其进行分析。本文的核心目标是对比mBERT与ParsBERT在Divar数据集上的性能表现。作者介绍了数据挖掘、波斯语以及这两种语言模型的相关背景，考察了数据集的构成与统计特征，并详细说明了两种方法的微调及训练配置。文中呈现了分析结果，明确了两种语言模型应用于波斯语文本数据时的优势与不足。本文为波斯语这类低资源语言的研究挑战与机遇，以及BERT这类先进语言模型分析此类数据的潜力提供了宝贵洞见。论文还阐释了数据挖掘流程，包括数据清洗、归一化技术等步骤。最后，本文讨论了监督学习、无监督学习、强化学习等机器学习问题类型，以及混淆矩阵等模式评估技术。总体而言，本文以Divar数据集为例，对语言模型与数据挖掘技术在低资源语言文本数据分析中的应用进行了详实的综述。
英文摘要：
This paper discusses the impact of the Internet on modern trading and the importance of data generated from these transactions for organizations to improve their marketing efforts. The paper uses the example of Divar, an online marketplace for buying and selling products and services in Iran, and presents a competition to predict the percentage of a car sales ad that would be published on the Divar website. Since the dataset provides a rich source of Persian text data, the authors use the Hazm library, a Python library designed for processing Persian text, and two state-of-the-art language models, mBERT and ParsBERT, to analyze it. The paper's primary objective is to compare the performance of mBERT and ParsBERT on the Divar dataset. The authors provide some background on data mining, Persian language, and the two language models, examine the dataset's composition and statistical features, and provide details on their fine-tuning and training configurations for both approaches. They present the results of their analysis and highlight the strengths and weaknesses of the two language models when applied to Persian text data. The paper offers valuable insights into the challenges and opportunities of working with low-resource languages such as Persian and the potential of advanced language models like BERT for analyzing such data. The paper also explains the data mining process, including steps such as data cleaning and normalization techniques. Finally, the paper discusses the types of machine learning problems, such as supervised, unsupervised, and reinforcement learning, and the pattern evaluation techniques, such as confusion matrix. Overall, the paper provides an informative overview of the use of language models and data mining techniques for analyzing text data in low-resource languages, using the example of the Divar dataset.
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。
