SkillSpan: Hard and Soft Skill Extraction from English Job Postings

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 domainadapted models significantly outperform their non-adapted counterparts, and single-task outperforms multi-task learning. * Equal contribution.You will thrive working in a Dev/Sec Ops culture . SKILL KNOWLEDGEThe ability to manage large sections of guests . SKILL Knowledge of property law rules of Germany .

A Novel Stream Mining Approach as Stream-Cluster Feature Tree Algorithm: A Case Study in Turkish Job Postings

“…For example, let the total number of clusters be 16. If we create the tree in a binary structure, the number of levels will be log 16 2 = 4. This means that we must carry out 2 × 4 = 8 comparison operations.…”

“…This means that we must carry out 2 × 4 = 8 comparison operations. If we create the tree in a triple structure, the number of levels will be log 16 3 = 2.5237. This means that we must carry out 3 × 2.5237 = 7.5711 comparison operations.…”

“…Studies analyzing job posting texts have been carried out using data from various countries and in various fields. Furthermore, a new study field, involving the extraction of soft skills and hard skills separately from job postings, has recently been expanding [16,17]. For example, while Zhang et al have created an original dataset containing soft and hard skills from job postings about the labor market [16], Lyu and Liu have determined soft and hard skills in the energy sector in their study [17].…”

Using Google Play Applications to Improve Language Preparatory School Students` Pronunciation Skills (A Study on Tishk International University Students in Erbil, Iraq)

“…In today's globalized society, a person with an exceptional command of any language might be considered for several lucrative employment opportunities in a variety of industries (Zhang et al, 2022). In many countries, it is a requirement for students to learn English in school, allowing them to take advantage of various job prospects in their home country or overseas upon graduation.…”

Soft Skills Demand and Supply Through the Lens of Higher Education Students

“…In contrast to hard skills, such as computation, language learning and so forth, which are gained through learning and can be quantifiable and measurable, soft skills learning and assessment are associated with significant pedagogical challenges since the evaluation of the soft skills is not explicit and straightforward (Petritsopoulou et al, 2021;Schislyaeva & Saychenko, 2022). Although the value and the need for assessing and enhancing soft skills are recognised on many occasions from the reports of labour market demands (Glinos et al, 2020;Zhang et al, 2022) to European Commission recommendations (Andrews & Higson, 2008), soft skills learning and assessment is not explicitly embedded in the education programs or professional development programs to date (Admin, 2022;Börner et al, 2018;Cinque, 2016).…”