In Hirize's AI-powered resume parsing system, skill identification is achieved through a combination of deep learning techniques, natural language processing (NLP), and extensive domain-specific knowledge bases.
Tokenization & Parsing: The AI first breaks down the text of a resume into individual words or 'tokens' using NLP techniques.
Deep Learning-Based Skill Identification: Deep learning algorithms are applied to identify tokens that represent skills. Our system has been trained on vast amounts of unlabelled data in an unsupervised manner, allowing it to learn complex patterns and features. The system is capable of identifying a token as a skill based on its understanding and learning from the training data.
Knowledge Bases and Contextual Understanding: The system cross-references identified skills with a predefined knowledge base of thousands of skills across various domains and industries. Additionally, the AI's capability to understand context helps in distinguishing a token as a skill based on its usage. For instance, the term 'Python' could refer to the programming language (a skill) or the snake, depending on the context.
Continuous Learning and Improvement: The deep learning model gets better with more data and experience, improving its skill identification capability over time. It can add new skills to its knowledge base as they emerge in the industry and become evident through the resumes it processes.
Data Sources: The unsupervised learning model has been trained on a substantial volume of anonymized resumes spanning various industries and roles, along with a wealth of unlabelled data. The 'knowledge base' used in skill identification is continuously updated based on industry trends, job descriptions, online courses, and certifications.
In summary, Hirize employs a combination of deep learning, unsupervised learning, and NLP to accurately identify and categorize skills on a resume, while its self-learning capability ensures it stays up-to-date with evolving industry needs.