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arXiv 2401.05073cs.LGcs.CL
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基于句子嵌入的招聘广告中横向技能分层分类

Hierarchical Classification of Transversal Skills in Job Ads Based on Sentence Embeddings

Florin Leon, Marius Gavrilescu, Sabina-Adriana Floria, Alina-Adriana Minea

备注：
19 pages, 6 figures, 6 tables, 43 references
发表信息：
Information, vol. 15, no. 3, article number 151, 18 pag., 2024
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AI总结：

本研究提出基于句子嵌入的招聘广告横向技能分层分类框架，结合ESCO分类标注、分层多标签策略与数据增强，对比发现英语专用与多语言嵌入模型准确率相近，验证了方法有效性及多语言模型对欧洲就业市场的适用性。

摘要：

本文提出一种分类框架，旨在识别招聘广告要求与横向技能集之间的关联，重点是利用深度学习模型预测单个职位描述所需的技能。该方法包括数据收集、预处理，以及使用ESCO（欧洲技能、能力与职业）分类体系进行标注。研究采用分层分类和多标签策略进行技能识别，同时通过增强技术解决数据不平衡问题，提升模型鲁棒性。针对英语专用句子嵌入模型与多语言句子嵌入模型的结果对比显示，二者准确率相近。实验案例研究详述了神经网络配置、超参数和交叉验证结果，凸显了分层方法的有效性，以及多语言模型对多元化欧洲就业市场的适用性。综上，本文提出了一种从招聘广告中分层分类横向技能的新方法。

英文摘要：

This paper proposes a classification framework aimed at identifying correlations between job ad requirements and transversal skill sets, with a focus on predicting the necessary skills for individual job descriptions using a deep learning model. The approach involves data collection, preprocessing, and labeling using ESCO (European Skills, Competences, and Occupations) taxonomy. Hierarchical classification and multi-label strategies are used for skill identification, while augmentation techniques address data imbalance, enhancing model robustness. A comparison between results obtained with English-specific and multi-language sentence embedding models reveals close accuracy. The experimental case studies detail neural network configurations, hyperparameters, and cross-validation results, highlighting the efficacy of the hierarchical approach and the suitability of the multi-language model for the diverse European job market. Thus, a new approach is proposed for the hierarchical classification of transversal skills from job ads.

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。

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