Jobbert Knowledge Extraction

🚀 技能提取模型演示項目

本項目是一個演示，使用了來自以下論文的模型。該模型聚焦於從英文招聘信息中進行技能提取，為勞動力市場動態分析提供了重要支持。

🚀 快速開始

本演示使用了來自以下論文的模型：

@inproceedings{zhang-etal-2022-skillspan,
    title = "{S}kill{S}pan: Hard and Soft Skill Extraction from {E}nglish Job Postings",
    author = "Zhang, Mike  and
      Jensen, Kristian  and
      Sonniks, Sif  and
      Plank, Barbara",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.366",
    doi = "10.18653/v1/2022.naacl-main.366",
    pages = "4962--4984",
    abstract = "Skill Extraction (SE) is an important and widely-studied task useful to gain insights into labor market dynamics. However, there is a lacuna of datasets and annotation guidelines; available datasets are few and contain crowd-sourced labels on the span-level or labels from a predefined skill inventory. To address this gap, we introduce SKILLSPAN, a novel SE dataset consisting of 14.5K sentences and over 12.5K annotated spans. We release its respective guidelines created over three different sources annotated for hard and soft skills by domain experts. We introduce a BERT baseline (Devlin et al., 2019). To improve upon this baseline, we experiment with language models that are optimized for long spans (Joshi et al., 2020; Beltagy et al., 2020), continuous pre-training on the job posting domain (Han and Eisenstein, 2019; Gururangan et al., 2020), and multi-task learning (Caruana, 1997). Our results show that the domain-adapted models significantly outperform their non-adapted counterparts, and single-task outperforms multi-task learning.",
}

請注意，還有另一個端點 jjzha/jobbert_skill_extraction。在本項目中，知識可視為硬技能，而技能則涵蓋軟技能和應用技能。