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Found 10 papers, 1 papers with code
Aspect-Based Sentiment Analysis for Open-Ended HR Survey Responses
Our approach aims to overcome the inherent noise and variability in these responses, enabling a comprehensive analysis of sentiments that can support employee lifecycle management.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline.
Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach
This study explores the potential of reinforcement learning algorithms to enhance career planning processes.
Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules
The increased digitization of the labour market has given researchers, educators, and companies the means to analyze and better understand the labour market.
conSultantBERT: Fine-tuned Siamese Sentence-BERT for Matching Jobs and Job Seekers
In this paper we focus on constructing useful embeddings of textual information in vacancies and resumes, which we aim to incorporate as features into job to job seeker matching models alongside other features.
Job Posting-Enriched Knowledge Graph for Skills-based Matching
In this paper we propose a custom-built Skills & Occupation Knowledge Graph (KG) that fits the above described dynamic nature of the labor market, by leveraging existing skills and occupation taxonomies enriched with external job posting data.
Entities of Interest
In the era of big data, we continuously - and at times unknowingly - leave behind digital traces, by browsing, sharing, posting, liking, searching, watching, and listening to online content.
Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation
In our first study we explore how our news recommender steers reading behavior in the context of editorial values such as serendipity, dynamism, diversity, and coverage.
Improving automated segmentation of radio shows with audio embeddings
Audio features have been proven useful for increasing the performance of automated topic segmentation systems.
The Birth of Collective Memories: Analyzing Emerging Entities in Text Streams
We do so by tracking entities that emerge in public discourse, that is, in online text streams such as social media and news streams, before they are incorporated into Wikipedia, which, we argue, can be viewed as an online place for collective memory.
