24 citations · 63 across the 44 of their papers we have counts for
45 papers
Robustness of transferability estimation metrics for medical imaging
Niclas Claßen, Théo Sourget, Dovile Juodelyte +2
In transfer learning, the choice of source model largely influences the performance on a target dataset. Still, selecting a fitting source remains a challenging task, especially in…
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…
Dataset Diversity Metrics and Impact on Classification Models
Théo Sourget, Niclas Claßen, Jack Junchi Xu +2
The diversity of training datasets is usually perceived as an important aspect to obtain a robust model. However, the definition of diversity is often not defined or differs across…
MultiLexNorm++: A Unified Benchmark and a Generative Model for Lexical Normalization for Asian Languages
Weerayut Buaphet, Thanh-Nhi Nguyen, Risa Kondo +8
Social media data has been of interest to Natural Language Processing (NLP) practitioners for over a decade, because of its richness in information, but also challenges for automat…
CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data
Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett +94
Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, especially on the noisy and heter…
Do Syntactic Categories Help in Developmentally Motivated Curriculum Learning for Language Models?
Arzu Burcu Güven, Anna Rogers, Rob van der Goot
We examine the syntactic properties of BabyLM corpus, and age-groups within CHILDES. While we find that CHILDES does not exhibit strong syntactic differentiation by age, we show th…
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…
NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching
Mike Zhang, Rob van der Goot
Matching job titles is a highly relevant task in the computational job market domain, as it improves e.g., automatic candidate matching, career path prediction, and job market anal…
DaKultur: Evaluating the Cultural Awareness of Language Models for Danish with Native Speakers
Max Müller-Eberstein, Mike Zhang, Elisa Bassignana +2
Large Language Models (LLMs) have seen widespread societal adoption. However, while they are able to interact with users in languages beyond English, they have been shown to lack c…
From Smør-re-brød to Subwords: Training LLMs on Danish, One Morpheme at a Time
Mikkel Wildner Kildeberg, Emil Allerslev Schledermann, Nicolaj Larsen +1
The best performing transformer-based language models use subword tokenization techniques, such as Byte-Pair-Encoding (BPE). However, these approaches often overlook linguistic pri…
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…
SnakModel: Lessons Learned from Training an Open Danish Large Language Model
Mike Zhang, Max Müller-Eberstein, Elisa Bassignana +1
We present SnakModel, a Danish large language model (LLM) based on Llama2-7B, which we continuously pre-train on 13.6B Danish words, and further tune on 3.7M Danish instructions. A…
data2lang2vec: Data Driven Typological Features Completion
Hamidreza Amirzadeh, Sadegh Jafari, Anika Harju +1
Language typology databases enhance multi-lingual Natural Language Processing (NLP) by improving model adaptability to diverse linguistic structures. The widely-used lang2vec toolk…
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…
Can Humans Identify Domains?
Maria Barrett, Max Müller-Eberstein, Elisa Bassignana +3
Textual domain is a crucial property within the Natural Language Processing (NLP) community due to its effects on downstream model performance. The concept itself is, however, loos…
Big City Bias: Evaluating the Impact of Metropolitan Size on Computational Job Market Abilities of Language Models
Charlie Campanella, Rob van der Goot
Large language models (LLMs) have emerged as a useful technology for job matching, for both candidates and employers. Job matching is often based on a particular geographic locatio…
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…
Establishing Trustworthiness: Rethinking Tasks and Model Evaluation
Robert Litschko, Max Müller-Eberstein, Rob van der Goot +2
Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades. Traditionall…
Findings of the VarDial Evaluation Campaign 2023
Noëmi Aepli, Çağrı Çöltekin, Rob Van Der Goot +7
This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2023. The campaign is part of the tenth workshop on Natural Language Proce…
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain
Mike Zhang, Rob van der Goot, Barbara Plank
The increasing number of benchmarks for Natural Language Processing (NLP) tasks in the computational job market domain highlights the demand for methods that can handle job-related…