"FIJO": a French Insurance Soft Skill Detection Dataset

• URL: http://arxiv.org/abs/2204.05208v1
• Date: Mon, 11 Apr 2022 15:54:22 GMT
• Title: "FIJO": a French Insurance Soft Skill Detection Dataset
• Authors: David Beauchemin and Julien Laumonier and Yvan Le Ster and Marouane Yassine
• Abstract summary: This article proposes a new public dataset, FIJO, containing insurance job offers, including many soft skill annotations. We present the results of skill detection algorithms using a named entity recognition approach and show that transformers-based models have good token-wise performances on this dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Understanding the evolution of job requirements is becoming more important for workers, companies and public organizations to follow the fast transformation of the employment market. Fortunately, recent natural language processing (NLP) approaches allow for the development of methods to automatically extract information from job ads and recognize skills more precisely. However, these efficient approaches need a large amount of annotated data from the studied domain which is difficult to access, mainly due to intellectual property. This article proposes a new public dataset, FIJO, containing insurance job offers, including many soft skill annotations. To understand the potential of this dataset, we detail some characteristics and some limitations. Then, we present the results of skill detection algorithms using a named entity recognition approach and show that transformers-based models have good token-wise performances on this dataset. Lastly, we analyze some errors made by our best model to emphasize the difficulties that may arise when applying NLP approaches.

Related papers

• Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning [0.0]
  We develop a robust pipeline to identify variables such as remote work availability, remuneration structures, educational requirements, and work experience preferences. Our methodology combines semantic chunking, retrieval-augmented generation (RAG), and fine-tuning DistilBERT models to overcome the limitations of traditional parsing tools. We present a comprehensive evaluation of our fine-tuned models and analyze their strengths, limitations, and potential for scaling.
  arXiv  Detail & Related papers  (2025-01-13T19:49:49Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters. We propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models [0.8899670429041453]
  We show that generative large language models (LLMs) can solve NLP tasks with very high quality without the need for extensive data. Based on a novel prompting strategy, we show that LLMs are able to outperform state-of-the-art machine learning approaches.
  arXiv  Detail & Related papers  (2024-07-26T06:39:35Z)
• Computational Job Market Analysis with Natural Language Processing [5.117211717291377]
  This thesis investigates Natural Language Processing (NLP) technology for extracting relevant information from job descriptions. We frame the problem, obtaining annotated data, and introducing extraction methodologies. Our contributions include job description datasets, a de-identification dataset, and a novel active learning algorithm for efficient model training.
  arXiv  Detail & Related papers  (2024-04-29T14:52:38Z)
• LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition [67.96794382040547]
  $LLM-DA$ is a novel data augmentation technique based on large language models (LLMs) for the few-shot NER task. Our approach involves employing 14 contextual rewriting strategies, designing entity replacements of the same type, and incorporating noise injection to enhance robustness.
  arXiv  Detail & Related papers  (2024-02-22T14:19:56Z)
• C-ICL: Contrastive In-context Learning for Information Extraction [54.39470114243744]
  c-ICL is a novel few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. Our experiments on various datasets indicate that c-ICL outperforms previous few-shot in-context learning methods.
  arXiv  Detail & Related papers  (2024-02-17T11:28:08Z)
• Capture the Flag: Uncovering Data Insights with Large Language Models [90.47038584812925]
  This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data. We propose a new evaluation methodology based on a "capture the flag" principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset.
  arXiv  Detail & Related papers  (2023-12-21T14:20:06Z)
• Extreme Multi-Label Skill Extraction Training using Large Language Models [19.095612333241288]
  We describe a cost-effective approach to generate an accurate, fully synthetic labeled dataset for skill extraction. Our results show a consistent increase of between 15 to 25 percentage points in textitR-Precision@5 compared to previously published results.
  arXiv  Detail & Related papers  (2023-07-20T11:29:15Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• Editing Large Language Models: Problems, Methods, and Opportunities [51.903537096207]
  This paper embarks on a deep exploration of the problems, methods, and opportunities related to model editing for LLMs. We provide an exhaustive overview of the task definition and challenges associated with model editing, along with an in-depth empirical analysis of the most progressive methods currently at our disposal. Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.
  arXiv  Detail & Related papers  (2023-05-22T16:00:00Z)
• Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction [19.43668931500507]
  We propose an end-to-end system for skill extraction, based on distant supervision through literal matching. We observe that using the ESCO taxonomy to select negative examples from related skills yields the biggest improvements. We release the benchmark dataset for research purposes to stimulate further research on the task.
  arXiv  Detail & Related papers  (2022-09-13T13:37:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.