This paper describes a method based on data from Wikipedia for the automatic semantic tagging of common and proper nouns in context. We first predict the semantic category of each Wikipedia entry using a rule-based method that detects definition patterns, and then we generalize from there using a statistical model that associates semantic categories with elements of the entry. The evaluation of proper and common nouns in Spanish reveals a general precision of .82 and a recall of .77. One feature of the method is its conceptual simplicity and computational efficiency. The implementation is offered as open-source code and the data used in the study is in the public domain.
This work focuses on the automatic recognition of location entities in Spanish clinical reports, using the MEDDOPLACE challenge (IberLEF 2023) as the experimental framework. We evaluated both general-domain pre-trained models and biomedical-specific models. Furthermore, we explored data augmentation techniques via back-translation and LLM-based paraphrase generation. Our results outperform previous state-of-the-art approaches, demonstrating the effectiveness of combining these data augmentation strategies with pre-trained clinical domain models.
Early detection of mental health risks on digital platforms is essential for enabling timely interventions. However, existing metrics such as Early Risk De-tection Error (ERDE) assess detection latency by message count rather than real time, misaligning with clinical priorities that depend on the temporal dynamics of risk. We propose Time-Aware Early Risk Detection Error (T-ERDE), a metric that replaces message-count penalties with temporal cost functions based on days elap-sed since the first user interaction. T-ERDE preserves compatibility with existing metrics while offering a more clinically meaningful and equitable evaluation. Expe-riments on the mentalRiskES (Spanish) and eRisk (English) datasets demonstrate that T-ERDE provides more accurate and relevant assessments, better capturing intervention urgency and reducing biases linked to user activity volume.
Despite advances in Natural Language Processing (NLP), figurative language remains a challenge for Large Language Models (LLMs). We evaluate and compare the performance of LLMs ChatGPT-4o, DeepSeek-V3, Gemini 2.5 Pro Experimental, and Grok 3 Preview in figurative language processing tasks. Drawing on the Conventional Figurative Language Theory (CFLT) proposed by Dobrovol'skij and Piirainen (2021), we compiled CLUE, an English-Spanish dataset of metaphors, idioms, similes, and proverbs. Using this dataset, we designed and conducted tests on figurative language detection, interpretation, and generation. The models exhibited strong performance in detection (0.94) and interpretation (0.91) tasks in both languages. However, with the exception of Gemini, their performance dropped significantly in the generation task (0.70), particularly in Spanish. We conclude that LLMs are effective in figurative language comprehension tasks but remain limited in their ability to produce it.
Media bias manifests through subtle editorial, discursive, and linguistic mechanisms that shape public perception without explicit falsehoods. Research on automatic media bias detection has focused largely on English resources and on binary, document-level labels, overlooking the hierarchical and perspectivist nature of bias. This article presents the Media Bias Bias-Mitigated Dataset (MBBMD), new corpus designed to address these limitations. MBBMD integrates three annotation levels: binary and multilab el document-level bias, and fine-grained sentence level manifestations. The annotation process combines a perspectivist document level scheme, preserving annotator disagreement, with a deterministic sentence-level procedure. The dataset also incorporates systematic Counterfactual Data Augmentation (CDA), enabling analyses of how such factors influence perceived bias. The sentence-level component comprises 2,348 annotated sentences covering six linguistic manifestations of media bias.
