This article will dive into the key aspects of legislation and why AI legislation is necessary We also explore the critical aspects of the EU AI Act and what it means for your organization.
In this article, we explore why Responsible AI is an important topic in recruitment and HR, what it entails, key questions when considering Responsible AI, and AI Legislation.
Leading the charge in recruitment technology, Textkernel’s LLM Parser offers transformative parsing solutions, enhancing industry standards with cutting-edge innovations.
AI is revolutionizing the recruitment industry, automating tasks like resume screening and scheduling interviews, freeing up recruiters to focus on building relationships and making informed hiring decisions. AI is empowering recruiters to work smarter, not replacing them. Embrace AI to stay ahead of the curve and hire the best talent for your organization.
Artificial Intelligence tools like ChatGPT are making waves by transforming workflows and revolutionizing industries, and the HR and recruitment industry is looking at the same possibilities. But what is this technology? And how does it relate to previous generations of AI?
Semantic search engines aim to understand what recruiters mean, rather than what they type. We discuss the benefits of this recruiting technology and the potential for semantic search engines in the future.
Main Software 50, the definitive benchmark award for software sector success in Benelux, has recognized Textkernel as the first overall Champion, a significant achievement for the AI-powered recruitment and talent management technology leader.
Akyla marks the second step in the international buy-and-build strategy of Textkernel since its management teamed up with strategic software investor Main Capital Partners (“Main”) in October 2020.
This is an article about building a large knowledge graph for the recruitment domain. It discusses what a knowledge graph is and the benefits of using one. The article also details how Textkernel’s knowledge graph is built and maintained. Some of the important points from this article are that the knowledge graph is used to improve the accuracy of Textkernel’s software and that it is constantly being updated.
The article discusses the problem of identifying and grouping duplicate job ads. The authors propose a system that uses shingling, min-wise permutation hashing, and inverted indexing to find job ads textually similar to a new input document. They also use machine learning and rule-based techniques to remove irrelevant content and identify text sections containing job description and candidate requirements. The system is able to find 90% of duplicates.
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