SkillNER: Mining and Mapping Soft Skills from any Text

• URL: http://arxiv.org/abs/2101.11431v1
• Date: Fri, 22 Jan 2021 11:14:05 GMT
• Title: SkillNER: Mining and Mapping Soft Skills from any Text
• Authors: Silvia Fareri, Nicola Melluso, Filippo Chiarello, Gualtiero Fantoni
• Abstract summary: In today's digital world there is an increasing focus on soft skills. Digitalisation has also increased the focus on soft skills, since such competencies are hardly acquired by Artificial Intelligence Systems. Despite this growing interest, researchers struggle in accurately defining the soft skill concept and in a complete and shared list of soft skills.
• Score: 2.580765958706854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In today's digital world there is an increasing focus on soft skills. The reasons are many, however the main ones can be traced down to the increased complexity of labor market dynamics and the shift towards digitalisation. Digitalisation has also increased the focus on soft skills, since such competencies are hardly acquired by Artificial Intelligence Systems. Despite this growing interest, researchers struggle in accurately defining the soft skill concept and in creating a complete and shared list of soft skills. Therefore, the aim of the present paper is the development of an automated tool capable of extracting soft skills from unstructured texts. Starting from an initial seed list of soft skills, we automatically collect a set of possible textual expressions referring to soft skills, thus creating a Soft Skills list. This has been done by applying Named Entity Recognition (NER) on a corpus of scientific papers developing a novel approach and a software application able to perform the automatic extraction of soft skills from text: the SkillNER. We measured the performance of the tools considering different training models and validated our approach comparing our list of soft skills with the skills labelled as transversal in ESCO (European Skills/Competence Qualification and Occupation). Finally we give a first example of how the SkillNER can be used, identifying the relationships among ESCO job profiles based on soft skills shared, and the relationships among soft skills based on job profiles in common. The final map of soft skills-job profiles may help accademia in achieving and sharing a clearer definition of what soft skills are and fuel future quantitative research on the topic.

Related papers

• A Multimodal Framework for Explainable Evaluation of Soft Skills in Educational Environments [0.0]
  This paper presents a fuzzy logic approach that employs a Granular Linguistic Model of Phenomena integrated with multimodal analysis to evaluate soft skills in undergraduate students.<n> Experiments were conducted with undergraduate students using a developed tool that assesses soft skills such as decision-making, communication, and creativity.
  arXiv  Detail & Related papers  (2025-05-03T11:54:35Z)
• Tec-Habilidad: Skill Classification for Bridging Education and Employment [0.7373617024876725]
  This paper develops a Spanish language dataset for skill extraction and classification. It provides annotation methodology to distinguish between knowledge, skill, and abilities. It also provides deep learning baselines to advance robust solutions for skill classification.
  arXiv  Detail & Related papers  (2025-03-05T22:05:42Z)
• Embracing Experiential Learning: Hackathons as an Educational Strategy for Shaping Soft Skills in Software Engineering [3.02243271391691]
  We report preliminary empirical evidence from a seven-day hybrid hackathon involving 40 students. We assess how the hackathon experience promoted innovative and creative thinking, collaboration and teamwork, and knowledge application among participants through a structured questionnaire designed to evaluate students' self-awareness. For industry, our discussion has implications around developing soft skills in future SE professionals, thereby enhancing their employability and readiness in the software market.
  arXiv  Detail & Related papers  (2025-02-11T20:58:33Z)
• MaestroMotif: Skill Design from Artificial Intelligence Feedback [67.17724089381056]
  MaestroMotif is a method for AI-assisted skill design, which yields high-performing and adaptable agents. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents.
  arXiv  Detail & Related papers  (2024-12-11T16:59:31Z)
• SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions [48.003320766433966]
  This work introduces Skill Discovery from Local Dependencies (Skild) Skild develops a novel skill learning objective that explicitly encourages the mastering of skills that induce different interactions within an environment. We evaluate Skild in several domains with challenging, long-horizon sparse reward tasks including a realistic simulated household robot domain.
  arXiv  Detail & Related papers  (2024-10-24T04:01:59Z)
• SkillMimic: Learning Basketball Interaction Skills from Demonstrations [85.23012579911378]
  We introduce SkillMimic, a unified data-driven framework that fundamentally changes how agents learn interaction skills. Our key insight is that a unified HOI imitation reward can effectively capture the essence of diverse interaction patterns from HOI datasets. For evaluation, we collect and introduce two basketball datasets containing approximately 35 minutes of diverse basketball skills.
  arXiv  Detail & Related papers  (2024-08-12T15:19:04Z)
• Language Guided Skill Discovery [56.84356022198222]
  We introduce Language Guided Skill Discovery (LGSD) to maximize semantic diversity between skills. LGSD takes user prompts as input and outputs a set of semantically distinctive skills. We demonstrate that LGSD enables legged robots to visit different user-intended areas on a plane by simply changing the prompt.
  arXiv  Detail & Related papers  (2024-06-07T04:25:38Z)
• NNOSE: Nearest Neighbor Occupational Skill Extraction [55.22292957778972]
  We tackle the complexity in occupational skill datasets. We employ an external datastore for retrieving similar skills in a dataset-unifying manner. We observe a performance gain in predicting infrequent patterns, with substantial gains of up to 30% span-F1 in cross-dataset settings.
  arXiv  Detail & Related papers  (2024-01-30T15:18:29Z)
• C$\cdot$ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters [49.83342243500835]
  We present C$cdot$ASE, an efficient framework that learns conditional Adversarial Skill Embeddings for physics-based characters. C$cdot$ASE divides the heterogeneous skill motions into distinct subsets containing homogeneous samples for training a low-level conditional model. The skill-conditioned imitation learning naturally offers explicit control over the character's skills after training.
  arXiv  Detail & Related papers  (2023-09-20T14:34:45Z)
• Large Language Models as Batteries-Included Zero-Shot ESCO Skills Matchers [0.0]
  We propose an end-to-end zero-shot system for skills extraction from job descriptions based on large language models (LLMs) We generate synthetic training data for the entirety of ESCO skills and train a classifier to extract skill mentions from job posts. We also employ a similarity retriever to generate skill candidates which are then re-ranked using a second LLM.
  arXiv  Detail & Related papers  (2023-07-07T12:04:12Z)
• Is it Required? Ranking the Skills Required for a Job-Title [0.0]
  We show that important/relevant skills appear more frequently in similar job titles. We train a Language-agnostic BERT Sentence (LaBSE) model to predict the importance of the skills using weak supervision.
  arXiv  Detail & Related papers  (2022-11-28T10:27:11Z)
• Choreographer: Learning and Adapting Skills in Imagination [60.09911483010824]
  We present Choreographer, a model-based agent that exploits its world model to learn and adapt skills in imagination. Our method decouples the exploration and skill learning processes, being able to discover skills in the latent state space of the model. Choreographer is able to learn skills both from offline data, and by collecting data simultaneously with an exploration policy.
  arXiv  Detail & Related papers  (2022-11-23T23:31:14Z)
• APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing [8.751819506454964]
  We propose a KT model, called APGKT, that exploits skill modes. We extract the subgraph topology of the skills involved in the question and combine the difficulty level of the skills to obtain the skill modes via encoding. We obtain a student's higher-order cognitive states of skills, which is used to predict the student's future answering performance.
  arXiv  Detail & Related papers  (2022-10-05T17:08:18Z)
• 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)
• Discovering Generalizable Skills via Automated Generation of Diverse Tasks [82.16392072211337]
  We propose a method to discover generalizable skills via automated generation of a diverse set of tasks. As opposed to prior work on unsupervised discovery of skills, our method pairs each skill with a unique task produced by a trainable task generator. A task discriminator defined on the robot behaviors in the generated tasks is jointly trained to estimate the evidence lower bound of the diversity objective. The learned skills can then be composed in a hierarchical reinforcement learning algorithm to solve unseen target tasks.
  arXiv  Detail & Related papers  (2021-06-26T03:41:51Z)
• ELSIM: End-to-end learning of reusable skills through intrinsic motivation [0.0]
  We present a novel reinforcement learning architecture which hierarchically learns and represents self-generated skills in an end-to-end way. With this architecture, an agent focuses only on task-rewarded skills while keeping the learning process of skills bottom-up.
  arXiv  Detail & Related papers  (2020-06-23T11:20:46Z)

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.