2 citations · 3 across the 10 of their papers we have counts for
11 papers
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…
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…
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…
Crossing Domains without Labels: Distant Supervision for Term Extraction
Elena Senger, Yuri Campbell, Rob van der Goot +1
Automatic Term Extraction (ATE) is a critical component in downstream NLP tasks such as document tagging, ontology construction and patent analysis. Current state-of-the-art method…
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…
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…
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…
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…
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…
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…
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…