Learning a taxonomy of job market skills from textual resources

从文本资源中学习就业市场技能的分类

• 批准号: 532040-2018
• 负责人: Lamontagne, Luc
• 金额: $ 1.82万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=656505
• 关键词: Learning; taxonomy; job; market; skills

The objective of this project consists of designing tools, using natural language processing and machine learning algorithms, to build automatically a taxonomy using the various skills accumulated in the databases of our industrial partner. This refers to the problem of taxonomy learning (also called taxonomy extraction), which consists of determining the concepts that supersede some others based on a corpus of text providing examples of how concepts might be related together. We will propose models, based on deep learning networks, to incorporate pattern-based features as well as distributional features to determine if two skills are related together. To determine candidate hypernyms that should be considered for a more detailed analysis, morphological analysis as well as statistical analysis (ex. co-occurrence analysis) will be applied. We will construct, train and compare different recurrent models to determine the architectures that are more suited to detect skills that are interrelated together. Distributional representations (i.e. word embeddings) for most of the skills present in the corporate database will be built. We will also extend current approaches to represent out of vocabulary words and compound terms. ********

该项目的目标包括设计工具，使用自然语言处理和机器学习算法，利用我们工业合作伙伴数据库中积累的各种技能自动构建分类法。这指的是分类学学习（也称为分类学提取）的问题，它包括基于文本语料库确定取代其他概念的概念，这些文本语料库提供了概念如何联系在一起的例子。我们将提出基于深度学习网络的模型，将基于模式的特征和分布特征结合起来，以确定两种技能是否相关。为了确定候选上位词，应该考虑进行更详细的分析，形态分析以及统计分析（例如。共现分析）。我们将构建、训练和比较不同的循环模型，以确定更适合检测相互关联的技能的架构。将为公司数据库中的大多数技能建立分布表示（即单词嵌入）。我们还将扩展当前的方法来表示词汇表外的单词和复合词。 ********