基于深度学习的大数据驱动元数据提取方法

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Panagiotis Skondras, Nikos Zotos, Dimitris Lagios, Panagiotis Zervas, Konstantinos C. Giotopoulos, Giannis Tzimas
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引用次数: 0

摘要

本文介绍了使用机器学习算法对招聘信息进行多类分类的研究。随着在线招聘平台的发展,大量的劳动力市场数据涌入。机器学习,尤其是NLP,越来越多地用于分析和分类招聘信息。然而,这些算法的有效性在很大程度上取决于训练数据的质量和数量。在我们的研究中,我们提出了一种针对招聘信息的多类分类方法,利用人工智能模型(如text- davincici -003)和量化版本的Falcon 7b (Falcon)、Wizardlm 7b (Wizardlm)和Vicuna 7b (Vicuna)来生成合成数据集。这些合成数据在两个用例场景中使用:(a)专门作为由合成职位发布组成的培训数据集(没有实际数据可用的情况),(b)作为增强方法来支持代表性不足的职位类别。为了评估我们提出的方法,我们依赖于两种成熟的方法:前馈神经网络(FFNN)和BERT模型。用例和训练方法都是根据真实的职位发布数据集进行评估的,以衡量分类的准确性。我们的实验证实了使用合成数据来增强职位分类的好处。在第一种情况下,模型的性能与真实数据相匹配,有时甚至超过真实数据。在第二个场景中,增强的类在大多数情况下都表现得更好。本研究证实了人工智能生成的数据集可以提高NLP算法的有效性,特别是在多类分类职位发布领域。虽然数据增强可以促进模型泛化,但其影响各不相同。这对于像FNN这样简单的模型特别有用。BERT由于其上下文感知架构,也从增强中受益,但改进有限。选择正确的增强类型和数量是至关重要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Approaches for Big Data-Driven Metadata Extraction in Online Job Postings
This article presents a study on the multi-class classification of job postings using machine learning algorithms. With the growth of online job platforms, there has been an influx of labor market data. Machine learning, particularly NLP, is increasingly used to analyze and classify job postings. However, the effectiveness of these algorithms largely hinges on the quality and volume of the training data. In our study, we propose a multi-class classification methodology for job postings, drawing on AI models such as text-davinci-003 and the quantized versions of Falcon 7b (Falcon), Wizardlm 7B (Wizardlm), and Vicuna 7B (Vicuna) to generate synthetic datasets. These synthetic data are employed in two use-case scenarios: (a) exclusively as training datasets composed of synthetic job postings (situations where no real data is available) and (b) as an augmentation method to bolster underrepresented job title categories. To evaluate our proposed method, we relied on two well-established approaches: the feedforward neural network (FFNN) and the BERT model. Both the use cases and training methods were assessed against a genuine job posting dataset to gauge classification accuracy. Our experiments substantiated the benefits of using synthetic data to enhance job posting classification. In the first scenario, the models’ performance matched, and occasionally exceeded, that of the real data. In the second scenario, the augmented classes consistently outperformed in most instances. This research confirms that AI-generated datasets can enhance the efficacy of NLP algorithms, especially in the domain of multi-class classification job postings. While data augmentation can boost model generalization, its impact varies. It is especially beneficial for simpler models like FNN. BERT, due to its context-aware architecture, also benefits from augmentation but sees limited improvement. Selecting the right type and amount of augmentation is essential.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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