Job application and assessment processes have evolved significantly in recent years, largely due to advancements in technology and changes in the way companies operate. Skill extraction and classification remain an important component of the modern hiring process as it provides a more objective way to evaluate candidates and automatically align their skills with the job requirements. However, to effectively evaluate the skills, the skill extraction tools must recognize varied mentions of skills on resumes, including direct mentions, implications, synonyms, acronyms, phrases, and proficiency levels, and differentiate between hard and soft skills. While tools like LLMs (Large Model Models) help extract and categorize skills from job applications, there's a lack of comprehensive datasets for evaluating the effectiveness of these models in accurately identifying and classifying skills in Spanish-language job applications. This gap hinders our ability to assess the reliability and precision of the models, which is crucial for ensuring that the selected candidates truly possess the required skills for the job. In this paper, we develop a Spanish language dataset for skill extraction and classification, provide annotation methodology to distinguish between knowledge, skill, and abilities, and provide deep learning baselines to advance robust solutions for skill classification.

Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in this application domain are skill extraction and classification from job postings. Because of its quick growth and its interdisciplinary nature, there is no exhaustive assessment of this emerging field. This survey aims to fill this gap by providing a comprehensive overview of deep learning methodologies, datasets, and terminologies specific to NLP-driven skill extraction and classification. Our comprehensive cataloging of publicly available datasets addresses the lack of consolidated information on dataset creation and characteristics. Finally, the focus on terminology addresses the current lack of consistent definitions for important concepts, such as hard and soft skills, and terms relating to skill extraction and classification.

The field of text classification, a fundamental subdomain within the natural language processing (NLP) field of machine learning (ML), has witnessed a remarkable evolution in recent years. With the exponential increase in textual data generated across various domains, the need for effective text classification methods has become increasingly pressing. Text classification is the task of assigning predefined labels or categories to textual documents based on their content. This task holds immense importance across various industries and applications, including but not limited to sentiment analysis, spam detection, content recommendation, and news classification. The ability to automatically organize and categorize large volumes of text can streamline information retrieval, enhance decision-making processes, and enable efficient data management. Traditional text classification methods rely on well-established techniques such as term frequency - inverse document frequency (TF-IDF) representations and traditional ML algorithms. TF-IDF measures the importance of each term within a document relative to a corpus of documents, providing a numerical representation of textual data.

Event detection is a critical feature in data-driven systems as it assists with the identification of nominal and anomalous behavior. Event detection is increasingly relevant in robotics as robots operate with greater autonomy in increasingly unstructured environments. In this work, we present an accurate, robust, fast, and versatile measure for skill and anomaly identification. A theoretical proof establishes the link between the derivative of the log-likelihood of the HMM filtered belief state and the latest emission probabilities. The key insight is the inverse relationship in which gradient analysis is used for skill and anomaly identification. Our measure showed better performance across all metrics than related state-of-the art works. The result is broadly applicable to domains that use HMMs for event detection.

I present skill bootstrapping, a proposed new research direction for agent learning and planning that allows an agent to start with low-level primitive actions, and develop skills that can be used for higher-level planning. Skills are developed over the course of solving many different problems in a domain, using reinforcement learning techniques to complement the benefits and disadvantages of heuristic-search planning. I describe the overall architecture of the proposed approach and discuss how it relates to other work.