Specialized languages can activate different sets of semantic features when compared to general language or express concepts through different words according to the domain. The specialized lexicon, i.e., lexical units that denote more specific concepts and knowledge emerging from specific domains, however, co-exists with the common lexicon, i.e., the set of lexical units that denote concepts and knowledge shared by the average speakers, regardless of their specific training or expertise. Communication between specialists and non-specialists can show a big gap between language(s), and therefore lexical units, used by the two groups. However, quite often, semantic and conceptual overlapping between specialized and common lexical units occurs and, in many cases, the specialized and common units refer to close concepts or even point to the same reality. Considering the modeling of meaning in functional lexical resources, this paper puts forth a solution that links common and specialized lexica within the WordNet model framework. We propose a new relation expressing semantic proximity between common and specialized units and define the conditions for its establishment. Besides contributing to the observation and understanding of the process of knowledge specialization and its reflex on the lexicon, the proposed relation allows for the integration of specialized and non-specialized lexicons into a single database, contributing directly to improving communication in specialist/non-specialist contexts, such as teaching–learning situations or health professional-patient interactions, among many others, where code-switching is frequent and necessary.

A exploração de corpora para a extração de léxico de especialidade é um método consensual e comum na construção de recursos lexicais. No entanto, as metodologias empregadas não são explicitamente discutidas, dificultando a comparação e a determinação de abordagens robustas. Para preencher essa lacuna, neste artigo apresentamos e discutimos uma metodologia detalhada para extração de léxico de especialidade a partir de corpora, conjugando abordagens linguísticas e estatísticas. O método proposto prevê tanto o uso de corpora de especialidade como de corpora monitores e inclui: i) análise de dados de frequência; ii) extração de concordâncias e colocações; iii) extração de informação de ordem textual, permitindo a extração de unidades lexicais atómicas e multipalavra e de relações semânticas relevantes. Desse modo, o objetivo da metodologia é a determinação de listas de potenciais unidades lexicais de especialidade e de informações relevantes para a sua descrição que permitam uma validação final rápida e eficiente, maximizando o valor informacional da interação com os especialistas.

This chapter presents research on the teaching-learning of Portuguese as a host language, based on the exploration of authentic informational and institutional texts targeting migrant and refugee people, and considering that successful host language teaching must correspond to the needs of its target audience. The chapter discusses methods of defining and identifying criteria and features to monitor official texts with regard to inclusiveness and bias. It provides insights on how to select real texts to be used in task-based language teaching approaches for inclusive host language teaching. Departing from a real corpus analysis, the potential and the limitations of existing guidelines to inclusiveness for the assessment of real texts are shown, as well as other still neglected issues. Furthermore, this chapter provides future research directions to an effective and reliable assessment of inclusive texts that can serve as inclusive host language teaching materials through NLP and machine learning approaches.