This study focuses on the changes that doctoral education has experienced in the last decades and discusses the role of doctoral supervisors. The figure of doctoral supervisor continues to be a subject of much debate; therefore, the aim of this study is to provide a universal, global, and common definition that clearly establishes the roles and functions of doctoral supervisors. Employing a multi-method approach, the study utilized the perspectives of linguistic relativism and prototype theory to understand how linguistic and label diversity may influence the perception and approach to supervisory tasks. We examine a corpus of 55 different labels to refer to “doctoral supervisor.” Data was collected from 116 countries, encompassing 47 different languages and 55 distinct labels from Europe, Africa, America, Asia, and Oceania, forming a unique corpus of information. The results reveal a total of 18 functions to be fulfilled by the doctoral supervisor. Additionally, the findings underscore the significance of linguistic influence in conceptualizing the functions associated with the supervisor in various cultural contexts and highlight the necessity for redefining the role of the thesis supervisor. The results hold potential benefits for doctoral schools and supervisors, serving as guidelines for standardizing the functions of the doctoral supervisor.

This chapter provides an overview of classical formal language theory. The text is focused on the definition of the fundamental concepts of language, grammar, and automata, and introduces some basic related notions. We also present the hierarchical classification of formal grammars proposed by N. Chomsky in the 1950s, known as the Chomsky hierarchy. The location of natural languages in this hierarchy is also discussed, together with the concept of Mildly Context Sensitivity. In the last part of the chapter, other formalisms that have interesting linguistic and computational properties are briefly introduced. The chapter concludes with a short review of grammatical inference, a subfield of machine learning that deals with the process of learning grammars and languages from data.

This paper aims at reviewing the most relevant linguistic applications developed in the intersection between three different fields: machine learning, formal language theory and agent technologies. On the one hand, we present some of the main linguistic contributions of the intersection between machine learning and formal languages, which constitutes a well-established research area known as Grammatical Inference. On the other hand, we present an overview of the main linguistic applications of models developed in the intersection between agent technologies and formal languages, such as colonies, grammar systems and eco-grammar systems. Our goal is to show how interdisciplinary research between these three fields can contribute to better understand how natural language is acquired and processed.

In recent years, the study of evaluative linguistic expressions has crossed the field of theoretical linguistics and has aroused interest in very different research areas such as artificial intelligence, psychology or cognitive linguistics. The interest in this type of expressions may be due to its relevance in applications such as opinion mining or sentiment analysis. This paper brings together Fuzzy Natural Logic and Fuzzy Property Grammars to approach evaluative expressions. Our contribution includes the marriage of mathematical and linguistic methods for providing a formalism to deal with the linguistic vagueness of evaluative expressions by describing the syntax and semantics of these structures. We contribute to the study of evaluative linguistic expressions by proposing a formal characterization of them where the concepts of semantic prime, borderline evaluative expressions and markedness are defined and where the relation between the semantic constraints of evaluations and their sentiment can be established. A proof-of-concept of how to create a lexicon of evaluative expressions for future computational applications is presented. The results demonstrate that linguistic evaluative expressions are gradient, have sentiment, and that the evaluations work as a relation of hypernym and hyponym, the hypernym being a semantic prime. Our findings provide the basis for building an ontology of evaluative expressions for future computational applications.

Within the area of Natural Language Processing, we approached the Author Profiling task as a text classification problem. Based on the author’s writing style, sociodemographic information, such as the author’s gender, age, or native language can be predicted. The exponential growth of user-generated data and the development of Machine-Learning techniques have led to significant advances in automatic gender detection. Unfortunately, gender detection models often become black-boxes in terms of interpretability. In this paper, we propose a tree-based computational model for gender detection made up of 198 features. Unlike the previous works on gender detection, we organized the features from a linguistic perspective into six categories: orthographic, morphological, lexical, syntactic, digital, and pragmatics-discursive. We implemented a Decision-Tree classifier to evaluate the performance of all feature combinations, and the experiments revealed that, on average, the classification accuracy increased up to 3.25% with the addition of feature sets. The maximum classification accuracy was reached by a three-level model that combined lexical, syntactic, and digital features. We present the most relevant features for gender detection according to the trees generated by the classifier and contextualize the significance of the computational results with the linguistic patterns defined by previous research in relation to gender.

This study aims to enhance the industrial employability of PhD graduates through the development of the HIRES-PhD framework, an abbreviation of High Impact Research and Employability Skills for the PhD. It identifies and categorizes essential transversal skills. Using a systematic literature review and thematic analysis, we screened 828 papers and selected 39 relevant studies to compile a database of 236 transversal skills. These skills were organized into 16 categories and further distilled into four overarching themes. The HIRES-PhD Framework is compared with traditional models like DOTS, USEM, and MCPHE, as well as recent European initiatives such as DocTalent4EU and OUTDOC. This comparison highlights the framework’s unique focus on doctoral training and industrial employability, unlike traditional models which often target broader educational contexts. Our findings emphasize the importance of a holistic approach to transversal skills training, tailored specifically to the needs of PhD graduates. The HIRES-PhD Framework serves as a comprehensive, data-driven tool for designing PhD programmes that align with industry demands, ensuring that doctoral training is relevant and effective in enhancing employability. In conclusion, the HIRES-PhD Framework significantly contributes to the improvement of doctoral education by providing a structured approach to transversal skills development, thus bridging the gap between academic training and industrial needs. This framework is a valuable resource for policymakers, educators, researchers, and employers aiming to optimize PhD programmes for better employment outcomes.