In this paper, we explore how large language models (LLMs) can be used to assign soft labels for metaphoricity in Popular Communication of Science (PCS) tweets written in Spanish. Instead of treating metaphors as a binary yes/no phenomenon, we focus on their graded nature and the variability commonly found in human annotations. Through a combination of prompt design and quantitative evaluation over a stratified sample of our dataset, we show that GPT-4 can assign probabilistic scores not only for general metaphoricity but also for specific metaphor types with consistency (Direct, Indirect, and Personification). The results show that, while LLMs align reasonably well with average human judgments for some categories, capturing the subtle patterns of inter-annotator disagreement remains a challenge. We present a corpus of 3,733 tweets annotated with LLM-generated soft labels, a valuable resource for further metaphor analysis in scientific discourse and figurative language annotation with LLMs.

Traditional linguistic annotation methods often strive for a gold standard with hard labels as input for natural language processing models, assuming an underlying objective truth for all tasks. However, disagreement among annotators is a common scenario, even for seemingly objective linguistic tasks, and is particularly prominent in figurative language annotation, since multiple valid interpretations can sometimes coexist. This study presents the annotation process for identifying metaphorical tweets within a corpus of 3733 Public Communication of Science texts written in Mexican Spanish, emphasizing inter-annotator disagreement. Using Fleiss’ and Cohen’s Kappa alongside agreement percentages, we evaluated metaphorical language detection through binary classification in three situations: two subsets of the corpus labeled by three different non-expert annotators each, and a subset of disagreement tweets, identified in the non-expert annotation phase, re-labeled by three expert annotators. Our results suggest that expert annotation may improve agreement levels, but does not exclude disagreement, likely due to factors such as the relatively novelty of the genre, the presence of multiple scientific topics, and the blending of specialized and non-specialized discourse. Going further, we propose adopting a learning-from-disagreement approach for capturing diverse annotation perspectives to enhance computational metaphor detection in Mexican Spanish.

This paper tackles SemEval~2025 Task~10, “Multilingual Characterization and Extraction of Narratives from Online News,” focusing on the Ukraine-Russia War and Climate Change domains. Our approach covers three subtasks: (1) {textbf{Entity Framing}}, assigning protagonist-antagonist-innocent roles with a prompt-based Llama~3.1~(8B) method; (2) {textbf{Narrative Classification}}, a multi-label classification using XLM-RoBERTa-base; and (3) {textbf{Narrative Extraction}}, generating concise, text-grounded explanations via FLAN-T5. Results show a unified multilingual transformer pipeline, combined with targeted preprocessing and fine-tuning, achieves substantial gains over baselines while effectively capturing complex narrative structures despite data imbalance and varied label distributions.

This paper addresses our approach to Task 11 (Track A and B) at the SemEval-2025, which focuses on the challenge of multilingual emotion detection in text, specifically identifying perceived emotions. The task is divided into tracks, we participated in two tracks: Track A, involving multilabel emotion detection, and Track B, which extends this to predicting emotion intensity on an ordinal scale. Addressing the challenges of imbalanced data and linguistic diversity, we propose a robust approach using pre-trained language models, fine-tuned with techniques such as extensive and deep hyperparameter optimization, along with loss function combinations to improve performance on imbalanced datasets and underrepresented languages. Our results demonstrate strong performance on Track A, particularly in low-resource languages such as Tigrinya (ranked 2nd), Igbo (ranked 3rd), and Oromo (ranked 4th). This work offers a scalable framework for emotion detection with applications in cross-cultural communication and human-computer interaction.

We present our approach to solving the task of identifying the effect of outdoor activities on social anxiety based on reddit posts. We employed state-of-the-art transformer models enhanced with a combination of advanced loss functions. Data augmentation techniques were also used to address class imbalance within the training set. Our method achieved a macro-averaged F1-score of 0.655 on the test data, surpassing the workshop’s mean F1-Score of 0.519. These findings suggest that integrating weighted loss functions improves the performance of transformer models in classifying unbalanced text data, while data augmentation can improve the model’s ability to generalize.

This paper addresses the shared task of multi-lingual emotion detection in tweets, presented at the Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis (WASSA) co-located with the ACL 2024 conference. The task involves predicting emotions from six classes in tweets from five different languages using only English for model training. Our approach focuses on addressing class imbalance through data augmentation, hierarchical classification, and the application of focal loss and weighted cross-entropy loss functions. These methods enhance our transformer-based model’s ability to transfer emotion detection capabilities across languages, resulting in improved performance despite the constraints of limited computational resources.

Word Association Norms (WAN) are collections that present stimuli words and the set of their associated responses. The corpus is widely used in diverse areas of expertise. In order to reduce the effort to have a good quality resource that can be reproduced in many languages with minimum sources, a methodology to build Automatic Word Association Norms is proposed (AWAN). The methodology has an input of two simple elements: a) dictionary, and b) pre-processed Word Embeddings. This new kind of WAN is evaluated in two ways: i) learning word embeddings based on the node2vec algorithm and comparing them with human annotated benchmarks, and ii) performing a lexical search for a reverse dictionary. Both evaluations are done in a weighted graph with the AWAN lexical elements. The results showed that the methodology produces good quality AWANs.

The computational treatment of temporal relations is based on the work of Allen, who establishes 13 different types, and Freksa, who designs a cognitive procedure to manage them. Freksa’s notation is not widely used because, although it has cognitive and expressive advantages, it is too complex from the computational perspective. This paper proposes a system for the annotation and management of temporal relations that combines the richness and expressiveness of Freksa’s approach with the simplicity of Allen’s notation. Our method is summarized in the application of bounding relations, thanks to which it is possible to obtain the temporary representation of complete neighborhoods capable of representing vague temporal relations such as those that can be frequently found in a text. Such advantages are obtained without the need to greatly increase the complexity of the labeling process since the markup language is almost the same as TimeML, to which only a second temporary “relType”’ type label relationship is added. Our experiments show that the temporal relationships that present vagueness are in fact much more common than those in which a single relationship can be established precisely. For these reasons, our new labeling system achieves a more agreeable representation of temporal relations.

Mexico is a Spanish speaking country that has a great language diversity, with 68 linguistic groups and 364 varieties. As they face a lack of representation in education, government, public services and media, they present high levels of endangerment. Due to the lack of data available on social media and the internet, few technologies have been developed for these languages. To analyze different linguistic phenomena in the country, the Language Engineering Group developed the Corpus Paralelo de Lenguas Mexicanas (CPLM) [The Mexican Languages Parallel Corpus], a collaborative parallel corpus for the low-resourced languages of Mexico. The CPLM aligns Spanish with six indigenous languages: Maya, Ch’ol, Mazatec, Mixtec, Otomi, and Nahuatl. First, this paper describes the process of building the CPLM: text searching, digitalization and alignment process. Furthermore, we present some difficulties regarding dialectal and orthographic variations. Second, we present the interface and types of searching as well as the use of filters.

In this paper, we show the enhancing of the Demanded Skills Diagnosis (DiCoDe: Diagnóstico de Competencias Demandadas), a system developed by Mexico City’s Ministry of Labor and Employment Promotion (STyFE: Secretaría de Trabajo y Fomento del Empleo de la Ciudad de México) that seeks to reduce information asymmetries between job seekers and employers. The project uses webscraping techniques to retrieve job vacancies posted on private job portals on a daily basis and with the purpose of informing training and individual case management policies as well as labor market monitoring. For this purpose, a collaboration project between STyFE and the Language Engineering Group (GIL: Grupo de Ingeniería Lingüística) was established in order to enhance DiCoDe by applying NLP models and semantic analysis. By this collaboration, DiCoDe’s job vacancies system’s macro-structure and its geographic referencing at the city hall (municipality) level were improved. More specifically, dictionaries were created to identify demanded competencies, skills and abilities (CSA) and algorithms were developed for dynamic classifying of vacancies and identifying terms for searches on free text, in order to improve the results and processing time of queries.

This work is about the compilation process of parallel documents Spanish-Mixtec. There are not many Spanish-Mixec parallel texts and most of the sources are non-digital books. Due to this, we need to face the errors when digitizing the sources and difficulties in sentence alignment, as well as the fact that does not exist a standard orthography. Our parallel corpus consists of sixty texts coming from books and digital repositories. These documents belong to different domains: history, traditional stories, didactic material, recipes, ethnographical descriptions of each town and instruction manuals for disease prevention. We have classified this material in five major categories: didactic (6 texts), educative (6 texts), interpretative (7 texts), narrative (39 texts), and poetic (2 texts). The final total of tokens is 49,814 Spanish words and 47,774 Mixtec words. The texts belong to the states of Oaxaca (48 texts), Guerrero (9 texts) and Puebla (3 texts). According to this data, we see that the corpus is unbalanced in what refers to the representation of the different territories. While 55% of speakers are in Oaxaca, 80% of texts come from this region. Guerrero has the 30% of speakers and the 15% of texts and Puebla, with the 15% of the speakers has a representation of the 5% in the corpus.

Indigenous languages of the American continent are highly diverse. However, they have received little attention from the technological perspective. In this paper, we review the research, the digital resources and the available NLP systems that focus on these languages. We present the main challenges and research questions that arise when distant languages and low-resource scenarios are faced. We would like to encourage NLP research in linguistically rich and diverse areas like the Americas.

This work presents the Sociolinguistic Corpus of WhatsApp Chats in Spanish among College Students, a corpus of raw data for general use. Its purpose is to offer data for the study of of language and interactions via Instant Messaging (IM) among bachelors. Our paper consists of an overview of both the corpus’s content and demographic metadata. Furthermore, it presents the current research being conducted with it —namely parenthetical expressions, orality traits, and code-switching. This work also includes a brief outline of similar corpora and recent studies in the field of IM.

This paper describes the project called Axolotl which comprises a Spanish-Nahuatl parallel corpus and its search interface. Spanish and Nahuatl are distant languages spoken in the same country. Due to the scarcity of digital resources, we describe the several problems that arose when compiling this corpus: most of our sources were non-digital books, we faced errors when digitizing the sources and there were difficulties in the sentence alignment process, just to mention some. The documents of the parallel corpus are not homogeneous, they were extracted from different sources, there is dialectal, diachronical, and orthographical variation. Additionally, we present a web search interface that allows to make queries through the whole parallel corpus, the system is capable to retrieve the parallel fragments that contain a word or phrase searched by a user in any of the languages. To our knowledge, this is the first Spanish-Nahuatl public available digital parallel corpus. We think that this resource can be useful to develop language technologies and linguistic studies for this language pair.

A scientific vocabulary is a set of terms that designate scientific concepts. This set of lexical units can be used in several applications ranging from the development of terminological dictionaries and machine translation systems to the development of lexical databases and beyond. Even though automatic term recognition systems exist since the 80s, this process is still mainly done by hand, since it generally yields more accurate results, although not in less time and at a higher cost. Some of the reasons for this are the fairly low precision and recall results obtained, the domain dependence of existing tools and the lack of available semantic knowledge needed to validate these results. In this paper we present a method that uses Wikipedia as a semantic knowledge resource, to validate term candidates from a set of scientific text books used in the last three years of high school for mathematics, health education and ecology. The proposed method may be applied to any domain or language (assuming there is a minimal coverage by Wikipedia).