activity
20232026
most citedDeep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings

2 citations · 3 across the 10 of their papers we have counts for

collaborators
Showing cs.CLShow all

10 papers · 1 filter

cs.CL2026

When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction

Ege Süalp, Elena Senger, Toine Bogers +1

Skills are widely assumed to play a central role in career transitions, motivating their use in career path prediction (CPP) systems. In this work, we critically examine whether ex…

cs.CL2026

Term-Centric Hierarchy Induction from Heterogeneous Corpora

Elena Senger, Yuri Campbell, Jan-Peter Bergmann +2

Organizing knowledge from diverse text sources into interpretable hierarchies is crucial for tasks such as policy analysis, innovation monitoring, and exploratory domain mapping. E…

cs.CL2026

When Meanings Meet: Investigating the Emergence and Quality of Shared Concept Spaces during Multilingual Language Model Training

Felicia Körner, Max Müller-Eberstein, Anna Korhonen +1

Training Large Language Models (LLMs) with high multilingual coverage is becoming increasingly important -- especially when monolingual resources are scarce. Recent studies have fo…

cs.CL2024

KARRIEREWEGE: A Large Scale Career Path Prediction Dataset

Elena Senger, Yuri Campbell, Rob van der Goot +1

Accurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path pr…

cs.CL2024

How to Encode Domain Information in Relation Classification

Elisa Bassignana, Viggo Unmack Gascou, Frida Nøhr Laustsen +4

Current language models require a lot of training data to obtain high performance. For Relation Classification (RC), many datasets are domain-specific, so combining datasets to obt…

cs.CL2024★ 2 cited

Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings

Elena Senger, Mike Zhang, Rob van der Goot +1

Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in th…

cs.CL2024

EEVEE: An Easy Annotation Tool for Natural Language Processing

Axel Sorensen, Siyao Peng, Barbara Plank +1

Annotation tools are the starting point for creating Natural Language Processing (NLP) datasets. There is a wide variety of tools available; setting up these tools is however a hin…

cs.CL2024

Entity Linking in the Job Market Domain

Mike Zhang, Rob van der Goot, Barbara Plank

In Natural Language Processing, entity linking (EL) has centered around Wikipedia, but yet remains underexplored for the job market domain. Disambiguating skill mentions can help u…

cs.CL2024★ 1 cited

NNOSE: Nearest Neighbor Occupational Skill Extraction

Mike Zhang, Rob van der Goot, Min-Yen Kan +1

The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text. With the advent of English benchmark job descriptio…

cs.CL2023

Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training

Max Müller-Eberstein, Rob van der Goot, Barbara Plank +1

Representational spaces learned via language modeling are fundamental to Natural Language Processing (NLP), however there has been limited understanding regarding how and when duri…