This paper presents a new web portal with information about the state of the art of natural language processing tasks in Spanish. It provides information about forums, competitions, tasks and datasets in Spanish, that would otherwise be spread in multiple articles and web sites. The portal consists of overview pages where information can be searched for and filtered by several criteria and individual pages with detailed information and hyperlinks to facilitate navigation. Information has been manually curated from publications that describe competitions and NLP tasks from 2013 until 2023 and will be updated as new tasks appear. A total of 185 tasks and 128 datasets from 94 competitions have been introduced.

How similar is the detection of media bias to the detection of persuasive techniques? We have explored how transferring knowledge from one task to the other may help to improve the performance. This paper presents the systems developed for participating in the SemEval-2023 Task 3: Detecting the Genre, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup. We have participated in both the subtask 1: News Genre Categorisation, and the subtask 3: Persuasion Techniques Detection. Our solutions are based on two-stage fine-tuned multilingual models. We evaluated our approach on the 9 languages provided in the task. Our results show that the use of transfer learning from media bias detection to persuasion techniques detection is beneficial for the subtask of detecting the genre (macro F1-score of 0.523 in the English test set) as it improves previous results, but not for the detection of persuasive techniques (micro F1-score of 0.24 in the English test set).

In Ordinal Classification tasks, items have to be assigned to classes that have a relative ordering, such as “positive”, “neutral”, “negative” in sentiment analysis. Remarkably, the most popular evaluation metrics for ordinal classification tasks either ignore relevant information (for instance, precision/recall on each of the classes ignores their relative ordering) or assume additional information (for instance, Mean Average Error assumes absolute distances between classes). In this paper we propose a new metric for Ordinal Classification, Closeness Evaluation Measure, that is rooted on Measurement Theory and Information Theory. Our theoretical analysis and experimental results over both synthetic data and data from NLP shared tasks indicate that the proposed metric captures quality aspects from different traditional tasks simultaneously. In addition, it generalizes some popular classification (nominal scale) and error minimization (interval scale) metrics, depending on the measurement scale in which it is instantiated.

This paper presents SentiSense, a concept-based affective lexicon. It is intended to be used in sentiment analysis-related tasks, specially in polarity and intensity classification and emotion identification. SentiSense attaches emotional meanings to concepts from the WordNet lexical database, instead of terms, thus allowing to address the word ambiguity problem using one of the many WordNet-based word sense disambiguation algorithms. SentiSense consists of 5,496 words and 2,190 synsets labeled with an emotion from a set of 14 emotional categories, which are related by an antonym relationship. SentiSense has been developed semi-automatically using several semantic relations between synsets in WordNet. SentiSense is endowed with a set of tools that allow users to visualize the lexicon and some statistics about the distribution of synsets and emotions in SentiSense, as well as to easily expand the lexicon. SentiSense is available for research purposes.