ABSTRACT Recent advances in text‐to‐image generation have enabled generative models to produce realistic visuals from textual descriptions, transforming creative workflows in domains like fashion. However, these systems may encode and reproduce societal biases, particularly in gender representation. This study proposes a systematic and interpretable methodology for analysing gender bias in text‐to‐image generation models. The framework is model‐agnostic and applicable to any generative system, combining quantitative evaluation with interpretable analysis. Our proposal is structured in two main components: (1) the creation of a controlled corpus; and (2) the evaluation of the generated outputs through manual annotations and three complementary analyses: (i) model neutrality, assessing gender balance under neutral prompts; (ii) model accuracy, measuring adherence to gendered instructions; and (iii) interpretable pattern discovery, uncovering the semantic attributes that drive gendered generations via decision tree modelling. Concretely, we focus on the fashion domain and employ Stable Diffusion as a representative state‐of‐the‐art text‐to‐image model, given the relevance of fashion and the scarcity of resources addressing bias in this field. To this end, we build a controlled corpus of 300 fashion‐related descriptions, each adapted into neutral, male and female versions. Empirically, experiments show that Stable Diffusion exhibits significant gender imbalances when generating images from neutral prompts, associating traditionally masculine outfits with male figures and traditionally feminine outfits with female figures. Theoretically, this methodology offers a reproducible approach for detecting and interpreting bias in multimodal generative models, and the resources created in this research are publicly available to scientific community, contributing to the development of fairer and more transparent AI systems.

Abstract The incorporation of machine learning algorithms into high‐risk decision‐making tasks has raised some alarms in the scientific community. Research shows that machine learning‐based technologies can contain biases that cause unfair decisions for certain population groups. The fundamental danger of ignoring this problem is that machine learning methods can not only reflect the biases present in our society but could also amplify them. This article presents the design and validation of a technology to assist the fair automation of classification problems. In essence, the proposal is based on taking advantage of the intermediate solutions generated during the resolution of classification problems through using Auto‐ML tools, in particular, AutoGOAL, to create unbiased/fair classifiers. The technology employs a multi‐objective optimization search to find the collection of models with the best trade‐offs between performance and fairness. To solve the optimization problem, we introduce a combination of Probabilistic Grammatical Evolution Search and NSGA‐II. The technology was evaluated using the Adult dataset from the UCI repository, a common benchmark in related research. Results were compared with other published results in scenarios with single and multiple fairness definitions. Our experiments demonstrate the technology's ability to automate classification tasks while incorporating fairness constraints. Additionally, our method achieves competitive results against other bias mitigation techniques. A notable advantage of our approach is its minimal requirement for machine learning expertise, thanks to its Auto‐ML foundation. This makes the technology accessible and valuable for advancing fairness in machine learning applications. The source code is available online for the research community.

Considering the potential of tools such as ChatGPT or Gemini to generate texts in a similar way to a human would do, having reliable detectors of AI –AI-generated content (AIGC)– is vital to combat the misuse and the surrounding negative consequences of those tools. Most research on AIGC detection has focused on the English language, often overlooking other languages that also have tools capable of generating human-like texts, such is the case of the Spanish language. This paper proposes a novel multilingual and multi-task approach for detecting machine versus human-generated text. The first task classifies whether a text is written by a machine or by a human, which is the research objective of this paper. The second task consists in detect the language of the text. To evaluate the results of our approach, this study has framed the scope of the AuTexTification shared task and also we have collected a different dataset in Spanish. The experiments carried out in Spanish and English show that our approach is very competitive concerning the state of the art, as well as it can generalize better, thus being able to detect an AI-generated text in multiple domains.

This work presents a standardised approach to create datasets for Science and Technology Parks (STPs), facilitating future analysis of STP characteristics, trends and performance. STPs are the most representative examples of innovation ecosystems. The ETL (extraction-transformation-load) structure was adapted to a global field study of STPs. A selection stage and quality check were incorporated, and the methodology was applied to Spanish STPs. This study applies diverse techniques such as expert labelling and information extraction which uses language technologies. A novel methodology for building quality and standardised STP datasets was designed and applied to a Spanish STP case study with 49 STPs. An updatable dataset and a list of the main features impacting STPs are presented. Twenty-one (n=21) core features were refined and selected, with fifteen of them (71.4%) being robust enough for developing further quality analysis. The methodology presented integrates different sources with heterogeneous information that is often decentralised, disaggregated and in different formats: excel files, and unstructured information in HTML or PDF format. The existence of this updatable dataset and the defined methodology will enable powerful AI tools to be applied that focus on more sophisticated analysis, such as taxonomy, monitoring, and predictive and prescriptive analytics in the innovation ecosystems field.

Disinformation is an important problem facing society nowadays. Given the rapid and easy access to information, news stories quickly go viral, the vast majority of which are misleading and with no prospect of verification. Specifically, the headline of a correctly designed news item must correspond to a summary of the main information of that news item and it should be neutral. However, many headlines circulating on the Internet use false or distorted information, seeking to confuse or mislead the reader. Misleading headlines indicate a dissonance between the headline and the content of the news story. From a computational perspective, this problem is being tackled as a Stance Detection problem between the headline and the body text of the news item. This paper contributes to the fight against the spread of misleading information by presenting a generic and flexible multi-level hierarchical classification. The approach is based on two stages that enable the detection of the stance between the news headline and the body text. The proposed architecture, called HeadlineStanceChecker+ uses the headline and only the essential information of the news item (not the full body text) as inputs. To extract this essential information, different summarization approaches (extractive and abstractive) are analyzed in order to determine the most relevant information for the task. The experimentation has been carried out using the Fake News Challenge (FNC-1) dataset. A 94.49% accuracy was obtained using extractive summaries, which were more helpful than abstractive ones. HeadlineStanceChecker+ improves the accuracy results of existing state-of-the-art systems. In conclusion, using automatic extractive summaries together with the two-stage generic architecture is an effective solution to the problem.

The headline of a news article is designed to succinctly summarize its content, providing the reader with a clear understanding of the news item. Unfortunately, in the post-truth era, headlines are more focused on attracting the reader’s attention for ideological or commercial reasons, thus leading to mis- or disinformation through false or distorted headlines. One way of combating this, although a challenging task, is by determining the relation between the headline and the body text to establish the stance. Hence, to contribute to the detection of mis- and disinformation, this paper proposes an approach (HeadlineStanceChecker) that determines the stance of a headline with respect to the body text to which it is associated. The novelty rests on the use of a two-stage classification architecture that uses summarization techniques to shape the input for both classifiers instead of directly passing the full news body text, thereby reducing the amount of information to be processed while keeping important information. Specifically, summarization is done through Positional Language Models leveraging on semantic resources to identify salient information in the body text that is then compared to its corresponding headline. The results obtained show that our approach achieves 94.31% accuracy for the overall classification and the best FNC-1 relative score compared with the state of the art. It is especially remarkable that the system, which uses only the relevant information provided by the automatic summaries instead of the whole text, is able to classify the different stance categories with very competitive results, especially in the discuss stance between the headline and the news body text. It can be concluded that using automatic extractive summaries as input of our approach together with the two-stage architecture is an appropriate solution to the problem.

Post-truth is a term that describes a distorting phenomenon that aims to manipulate public opinion and behavior. One of its key engines is the spread of Fake News. Nowadays most news is rapidly disseminated in written language via digital media and social networks. Therefore, to detect fake news it is becoming increasingly necessary to apply Artificial Intelligence (AI) and, more specifically Natural Language Processing (NLP). This paper presents a review of the application of AI to the complex task of automatically detecting fake news. The review begins with a definition and classification of fake news. Considering the complexity of the fake news detection task, a divide-and-conquer methodology was applied to identify a series of subtasks to tackle the problem from a computational perspective. As a result, the following subtasks were identified: deception detection; stance detection; controversy and polarization; automated fact checking; clickbait detection; and, credibility scores. From each subtask, a PRISMA compliant systematic review of the main studies was undertaken, searching Google Scholar. The various approaches and technologies are surveyed, as well as the resources and competitions that have been involved in resolving the different subtasks. The review concludes with a roadmap for addressing the future challenges that have emerged from the analysis of the state of the art, providing a rich source of potential work for the research community going forward.

Abstract In this paper, we present a Text Summarisation tool,compendium, capable of generating the most common types of summaries. Regarding the input, single- and multi-document summaries can be produced; as the output, the summaries can be extractive or abstractive-oriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture forcompendiumis divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions ofcompendiumwith respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows thatcompendiumis very competitive and appropriate to be used as a tool for generating summaries.

As the Internet grows, it becomes essential to find efficient tools to deal with all the available information. Question answering (QA) and text summarization (TS) research fields focus on presenting the information requested by users in a more concise way. In this paper, the appropriateness and benefits of using summaries in semantic QA are analyzed. For this purpose, a combined approach where a TS component is integrated into a Web-based semantic QA system is developed. The main goal of this paper is to determine to what extent TS can help semantic QA approaches, when using summaries instead of search engine snippets as the corpus for answering questions. In particular, three issues are analyzed: (i) the appropriateness of query-focused (QF) summarization rather than generic summarization for the QA task, (ii) the suitable length comparing short and long summaries, and (iii) the benefits of using TS instead of snippets for finding the answers, tested within two semantic QA approaches (named entities and semantic roles). The results obtained show that QF summarization is better than generic (58% improvement), short summaries are better than long (6.3% improvement), and the use of TS within semantic QA improves the performance for both named-entity-based (10%) and, especially, semantic-role-based QA (47.5%). © 2011 Wiley Periodicals, Inc.

In this paper we concentrate on the resolution of the lexical ambiguity that arises when a given word has several different meanings. This specific task is commonly referred to as word sense disambiguation (WSD). The task of WSD consists of assigning the correct sense to words using an electronic dictionary as the source of word definitions. We present two WSD methods based on two main methodological approaches in this research area: a knowledge-based method and a corpus-based method. Our hypothesis is that word-sense disambiguation requires several knowledge sources in order to solve the semantic ambiguity of the words. These sources can be of different kinds--- for example, syntagmatic, paradigmatic or statistical information. Our approach combines various sources of knowledge, through combinations of the two WSD methods mentioned above. Mainly, the paper concentrates on how to combine these methods and sources of information in order to achieve good results in the disambiguation. Finally, this paper presents a comprehensive study and experimental work on evaluation of the methods and their combinations.

One of the main problems in integrating databases into a common repository is the possible inconsistency of the values stored in them, i.e., the very same term may have different values, due to misspelling, a permuted word order, spelling variants and so on. The authors present an automatic method for reducing inconsistency found in existing databases, and thus, improving data quality. All the values that refer to a same term are clustered by measuring their degree of similarity. The clustered values can be assigned to a common value that, in principle, could be substituted for the original values. We evaluate four different similarity measures for clustering with and without expansion of abbreviations. The method we propose may work well in practice but it is time-consuming. In order to reduce this problem, we remove stop words for speeding up the clustering.

This paper presents an algorithm for identifying noun phrase antecedents of third person personal pronouns, demonstrative pronouns, reflexive pronouns, and omitted pronouns (zero pronouns) in unrestricted Spanish texts. We define a list of constraints and preferences for different types of pronominal expressions, and we document in detail the importance of each kind of knowledge (lexical, morphological, syntactic, and statistical) in anaphora resolution for Spanish. The paper also provides a definition for syntactic conditions on Spanish NP-pronoun noncoreference using partial parsing. The algorithm has been evaluated on a corpus of 1,677 pronouns and achieved a success rate of 76.8%. We have also implemented four competitive algorithms and tested their performance in a blind evaluation on the same test corpus. This new approach could easily be extended to other languages such as English, Portuguese, Italian, or Japanese.

This paper presents an algorithm for identifying noun-phrase antecedents of pronouns and adjectival anaphors in Spanish dialogues. We believe that anaphora resolution requires numerous sources of information in order to find the correct antecedent of the anaphor. These sources can be of different kinds, e.g., linguistic information, discourse/dialogue structure information, or topic information. For this reason, our algorithm uses various different kinds of information (hybrid information). The algorithm is based on linguistic constraints and preferences and uses an anaphoric accessibility space within which the algorithm finds the noun phrase. We present some experiments related to this algorithm and this space using a corpus of 204 dialogues. The algorithm is implemented in Prolog. According to this study, 95.9% of antecedents were located in the proposed space, a precision of 81.3% was obtained for pronominal anaphora resolution, and 81.5% for adjectival anaphora.

The aim of our GOLD model ([7], [9]) is to provide an Object Oriented (OO) Multidimensional data model supported by an OO formal specification language that allows us to automatically generate prototypes from the specification at the conceptual level, and therefore, to animate and check system properties. Within the context of OO modeling and automatic prototyping, the basis of the mapping from modeling to programming is focused on the identification of (cardinality and behavioral) patterns in the design phase and their relationships with the data model, process model and interface design.

The problem with using extensive lexical, syntactic and semantic resources is the large quantity of information that is provided, as well as all the different senses that each word has. To contribute to the resolution of this problem, this paper presents a tool for knowledge acquisition from WordNet, restricting and limiting the learning of word senses. For this purpose, a semantically restricted corpus (Brown Corpus semantic concordance files from the WordNet package) is used, and a restricted "subnet" is obtained, with the word senses related to the text domain.

. In the recent past, there has been an increasing interest in multidimensional databases (MDB) and On-line Analytical Processing (OLAP) scenarios. Several multidimensional models have been proposed in the last days. However, very few works have been focused on the area of multidimensional database conceptual modeling. Moreover, they are either conceptual extensions to the classical multidimensional model or translations from classical database conceptual models (such as the EntityRelationship model). Nevertheless, we take the concepts and basic ideas of the classical multidimensional model (dimensions and facts) to propose a revolutionary approach based on the Object Oriented (OO) Paradigm to MDB conceptual modeling. Then, the basic elements of our Object Oriented Multidimensional Model (OOMD) such as dimension classes and fact classes are introduced. We then present cube classes as the basic structure to allow a subsequent analysis of the data stored in the system. We fairly believe ...