David Graus

Lois Rink, our former MSc data science intern at Randstad Groep Nederland, has published her thesis at the 1st Workshop on Natural Language Processing for Human Resources (NLP4HR), to be held at EACL 2024.

Lois’ thesis is titled Aspect-Based Sentiment Analysis for Open-Ended HR Survey Responses, and was written under supervision of Job Meijdam and myself. Get the preprint here or on arXiv:

• L. Rink, J. Meijdam, and D. Graus, “Aspect-based sentiment analysis for open-ended HR survey responses,” in 1st workshop on natural language processing for human resources, 2024.
  [Bibtex]

  @inproceedings{rink2024aspectbased,
  title={Aspect-Based Sentiment Analysis for Open-Ended {HR} Survey Responses},
  author={Lois Rink and Job Meijdam and David Graus},
  booktitle={1st Workshop on Natural Language Processing for Human Resources},
  year={2024}
  }

Abstract:

Understanding preferences, opinions, and sentiment of the workforce is paramount for effective employee lifecycle management. Open-ended survey responses serve as a valuable source of information. This paper proposes a machine learning approach for aspect-based sentiment analysis (ABSA) of Dutch open-ended responses in employee satisfaction surveys. 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. Through response clustering we identify six key aspects (salary, schedule, contact, communication, personal attention, agreements), which we validate by domain experts. We compile a dataset of 1,458 Dutch survey responses, revealing label imbalance in aspects and sentiments. We propose few-shot approaches for ABSA based on Dutch BERT models, and compare them against bag-of-words and zero-shot baselines. Our work significantly contributes to the field of ABSA by demonstrating the first successful application of Dutch pre-trained language models to aspect-based sentiment analysis in the domain of human resources (HR).

For this year’s edition (the third in a row) of the Recommender Systems in Human Resources workshop, to be held at the ACM RecSys Conference in Singapore, I co-authored three accepted papers:

Enhancing Resume Content Extraction in Question Answering Systems through T5 Model Variant

• Y. Luo, F. Lu, V. Pal, and D. Graus, “Enhancing resume content extraction in question answering systems through t5 model variants,” in Recsys in hr’23: the 3\textsuperscriptrd workshop on recommender systems for human resources, 2023.
  [Bibtex]

  @inproceedings{luo2023enhancing,
  title={Enhancing Resume Content Extraction in Question Answering Systems through T5 Model Variants},
  author={Yuxin Luo and Feng Lu and Vaishali Pal and David Graus},
  year={2023},
  booktitle = {RecSys in HR’23: The 3\textsuperscript{rd} Workshop on Recommender Systems for Human Resources},
  numpages = {10},
  location = {Singapore},
  series = {CEUR Workshop Proceedings},
  month={9}
  }

This paper is based on the MSc Data Science thesis of Yuxin, who was supervised by Feng. Yuxin applied Large Language Models (mT5) to do Question Answering over resumes for information extraction.

Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach

• S. Avlonitis, D. Lavi, M. Mansoury, and D. Graus, “Career path recommendations for long-term income maximization: a reinforcement learning approach,” in Recsys in hr’23: the 3\textsuperscriptrd workshop on recommender systems for human resources, 2023.
  [Bibtex]

  @inproceedings{avlonitis2023career,
  title={Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach},
  author={Spyros Avlonitis and Dor Lavi and Masoud Mansoury and David Graus},
  year={2023},
  booktitle = {RecSys in HR’23: The 3\textsuperscript{rd} Workshop on Recommender Systems for Human Resources},
  numpages = {8},
  location = {Singapore},
  series = {CEUR Workshop Proceedings},
  month={9}
  }

This is Spyros’ MSc AI thesis (from 2022), who was jointly supervised by me and Dor. Spyros applied reinforcement learning to career path recommendations, leveraging Randstad’s rich data for simulating an environment in which agents can apply for jobs, be hired, and receive salary.

Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules

• J. Vrolijk and D. Graus, “Enhancing PLM performance on labour market tasks via instruction-based finetuning and prompt-tuning with rules,” in Recsys in hr’23: the 3\textsuperscriptrd workshop on recommender systems for human resources, 2023.
  [Bibtex]

  @inproceedings{vrolijk2023enhancing,
  title={Enhancing {PLM} Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules},
  author={Jarno Vrolijk and David Graus},
  year={2023},
  booktitle = {RecSys in HR’23: The 3\textsuperscript{rd} Workshop on Recommender Systems for Human Resources},
  numpages = {10},
  location = {Singapore},
  series = {CEUR Workshop Proceedings},
  month={9}
  }

This paper is based on work by Jarno on using structured taxonomy data for training and fine-tuning Large Language Models for different downstream tasks (such as relation classification, entity linking, and question answering).

See the full list of accepted papers.