DataOps for Societal Intelligence: a Data Pipeline for Labor Market
Skills Extraction and Matching
- URL: http://arxiv.org/abs/2104.01966v1
- Date: Mon, 5 Apr 2021 15:37:25 GMT
- Title: DataOps for Societal Intelligence: a Data Pipeline for Labor Market
Skills Extraction and Matching
- Authors: Damian Andrew Tamburri, Willem-Jan Van den Heuvel, Martin Garriga
- Abstract summary: We formulate and solve this problem using DataOps models.
We then focus on the critical task of skills extraction from resumes.
We showcase preliminary results with applied machine learning on real data.
- Score: 5.842787579447653
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Big Data analytics supported by AI algorithms can support skills localization
and retrieval in the context of a labor market intelligence problem. We
formulate and solve this problem through specific DataOps models, blending data
sources from administrative and technical partners in several countries into
cooperation, creating shared knowledge to support policy and decision-making.
We then focus on the critical task of skills extraction from resumes and
vacancies featuring state-of-the-art machine learning models. We showcase
preliminary results with applied machine learning on real data from the
employment agencies of the Netherlands and the Flemish region in Belgium. The
final goal is to match these skills to standard ontologies of skills, jobs and
occupations.
Related papers
- 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) - 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) - 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) - A Study of Situational Reasoning for Traffic Understanding [63.45021731775964]
We devise three novel text-based tasks for situational reasoning in the traffic domain.
We adopt four knowledge-enhanced methods that have shown generalization capability across language reasoning tasks in prior work.
We provide in-depth analyses of model performance on data partitions and examine model predictions categorically.
arXiv Detail & Related papers (2023-06-05T01:01:12Z) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - Human-Centric Multimodal Machine Learning: Recent Advances and Testbed
on AI-based Recruitment [66.91538273487379]
There is a certain consensus about the need to develop AI applications with a Human-Centric approach.
Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes.
We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
arXiv Detail & Related papers (2023-02-13T16:44:44Z) - A practical method for occupational skills detection in Vietnamese job
listings [0.16114012813668932]
Lack of accurate and timely labor market information leads to skill miss-matches.
Traditional approaches rely on existing taxonomy and/or large annotated data.
We propose a practical methodology for skill detection in Vietnamese job listings.
arXiv Detail & Related papers (2022-10-26T10:23:18Z) - 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) - The Data-Production Dispositif [0.0]
This paper investigates outsourced machine learning data work in Latin America by studying three platforms in Venezuela and a BPO in Argentina.
We lean on the Foucauldian notion of dispositif to define the data-production dispositif as an ensemble of discourses, actions, and objects strategically disposed to (re)produce power/knowledge relations in data and labor.
We conclude by stressing the importance of counteracting the data-production dispositif by fighting alienation and precarization, and empowering data workers to become assets in the quest for high-quality data.
arXiv Detail & Related papers (2022-05-24T10:51:05Z) - "FIJO": a French Insurance Soft Skill Detection Dataset [0.0]
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.
arXiv Detail & Related papers (2022-04-11T15:54:22Z) - From Distributed Machine Learning to Federated Learning: A Survey [49.7569746460225]
Federated learning emerges as an efficient approach to exploit distributed data and computing resources.
We propose a functional architecture of federated learning systems and a taxonomy of related techniques.
We present the distributed training, data communication, and security of FL systems.
arXiv Detail & Related papers (2021-04-29T14:15:11Z)
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.