Currently, governments and many other institutions have been making significant efforts to promote financial literacy. However, a considerable portion of the population still lacks basic financial knowledge, highlighting the need for updated strategies to enhance financial education. In today's digital world - where people often search for quick and convenient solutions - the development of a reliable and intelligent chatbot to answer questions related to financial concepts and decision-making can be very beneficial. This paper proposes the implementation of an automated web scraper to extract content from a trustworthy financial education website with plenty of useful concepts about finances, using this collected data to develop a chatbot which provides accurate and helpful responses to users. The solution is built using technologies such as Streamlit, Langchain, and OpenAI.
In this paper, we introduce elements for MoSCO, a framework for building hybrid metaheuristic-based solvers from a collection of reusable base components. The framework is implemented in Julia and provides a modular architecture for composing solvers through a pipeline-based approach. The modular design of MoSCO supports the creation of reusable components and adaptable solver strategies for various Constraint Satisfaction Problems (CSPs) and Constraint Optimization Problems (COPs). We validate MoSCO's utility through practical examples, demonstrating its effectiveness in reconstructing established metaheuristics and enabling the creation of novel solver configurations. This work lays the foundation for future developments in automated solver construction and parameter optimization.
This study analyzes the social media discourse of leading figures from Portugal's far right party CHEGA, examining 10,323 posts on X (formerly Twitter) published between late 2019 and mid-2024. Using BERTopic, 59 latent topics clustered into two main discursive dynamics were found: (1) ideological and public, and (2) party, electoral and parliamentary related. Within the first dynamic, we conducted a focused sub-analysis of themes related with identity, immigration and security narratives - topics that display posting peaks around electoral cycles, suggesting the strategic use of emotionally charged, identitarian frames for political mobilization. The model exhibits strong topic coherence and lexical diversity, indicating its robustness in extracting thematic structures from politically polarized microtexts. Nevertheless, our findings are constrained by source, the absence of interaction metrics, and the unmet need to link online discourse to offline events. This study demonstrates how computational topic modeling can reveal strategic communication patterns in far-right political discourse and underscores the need for cross-platform and interaction-level research to assess broader societal impact.
This study examines the intersection of sociodemographic characteristics, linguistic features, and writing placement outcomes at a community college in the United States of America. It focuses on 210 anonymized writing samples from native English speakers (L1) that were automatically classified by ACCUPLACER and independently assessed by two trained raters. Disparities across gender and race using 40 top-ranked linguistic features selected from Coh-Metrix, CTAP, and Developmental Education-Specific (DES) sets were analyzed. Three statistical tests were used: one-way ANOVA, Tukey's HSD, and Chi-square. ANOVA results showed racial differences in nine linguistic features, especially those tied to syntactic complexity, discourse markers, and lexical precision. Gender differences were more limited, with only one feature reaching significance (Positive Connectives, p = 0.007). Tukey's HSD pairwise tests showed no significant gender group variation but revealed sensitivity in DES features when comparing racial groups. Chi-square analysis indicated no significant association between gender and placement outcomes but suggested a possible link between race and human-assigned levels (chi(2) = 9.588, p = 0.048). These findings suggest that while automated systems assess general writing skills, human-devised linguistic features and demographic insights can support more equitable placement practices for all students entering college-level programs.
Semantic parsing serves as a crucial interface between natural language and formal meaning representations, enabling computational systems to capture the underlying semantic structure of linguistic expressions. This paper addresses a relatively understudied area in both linguistic theory and natural language processing: the semantic representation of adverbs. We conduct a comparative analysis of annotation guidelines and practices across two semantic representation frameworks: Lexicalized Meaning Representation (LMR), applied to the European Portuguese edition of the novella "O Principezinho" by Antoine de Saint-Exupery (1943); and Abstract Meaning Representation (AMR), applied to the Brazilian Portuguese edition, "O Pequeno Principe". The study reveals significant limitations in AMR's handling of adverbial constructions, particularly when assessed against contemporary syntactic-semantic advances in linguistic theory. Furthermore, it highlights the theoretical and practical challenges that LMR continues to face in this domain.
