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Recent research has shown that the unaware automation of high-risk decision-making tasks can result in unfair decisions being made. The most common approaches to address this problem adopt definitions of fairness based on protected attributes. Precise annotation of protected attributes enables the application of bias mitigation techniques to commonly unlabeled kinds of data (e.g., images, text, etc.). This paper proposes a framework to automatically annotate protected attributes in data collections. The framework focuses on providing a single interface to annotate protected attributes of different types (e.g., gender, race, etc.) and from different kinds of data. Internally, the framework coordinates multiple sensors to produce the final annotation. Several sensors for textual data are proposed. An optimization search technique is designed to tune the framework to specific domains. Additionally, a small dataset of movie reviews —annotated with gender and sentiment— was created. The evaluation in datasets of texts from diverse domains shows the quality of the annotations and their effectiveness to be used as a proxy to estimate fairness in datasets and machine learning models. The source code is available online for the research community.

Los modelos de aprendizaje automático están siendo ampliamente utilizados en múltiples áreas de la vida humana. Tradicionalmente, se han aplicado en reconocimiento de voz, detección de rostros, clasificación de imágenes, sistemas de recomendación, etc. Con la reciente revolución de los modelos generativos, la popularidad de los chatbots conversacionales se ha disparado. Esto ha dado lugar a que los modelos de aprendizaje automático se utilicen cada vez más para abordar tareas para las que no estaban específicamente capacitados. El prompt engineering ha permitido que personas no expertas en aprendizaje automático (que comúnmente tampoco están familiarizadas con los problemas subyacentes al uso de modelos de aprendizaje automático para hacer predicciones) automaticen ciertas tareas. La incorporación de algoritmos de aprendizaje automático en tareas de toma de decisiones de alto riesgo ha levantado algunas alertas en la comunidad científica. Las tareas de toma de decisiones de alto riesgo denotan aquellas tareas que pueden tener un gran impacto en las vidas de las personas sobre quienes se toman las decisiones. Por ejemplo, se han utilizado modelos para decidir si una persona es contratada o no, si se le concede un préstamo, si se acepta una solicitud de cobertura ampliada de seguridad sanitaria y para predecir la probabilidad de reincidencia en un delito. Estudios han demostrado que la automatización inconsciente de este tipo de tareas contiene sesgos, lo cual provoca que decisiones injustas sean tomadas sobre determinados grupos de población. El peligro fundamental de ignorar este problema es que los métodos de aprendizaje automático podrían no sólo reflejar los sesgos presentes en nuestra sociedad, sino que también amplificarlos. Esta tesis presenta el diseño y validación de una tecnología para asistir la automatización justa de problemas de clasificación. En esencia, la propuesta se basa en diseñar una tecnología que saque provecho de las soluciones intermedias gen

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.

Numerical information in written texts impacts their readability and is considered complex for people with cognitive disabilities by the Easy-to-Read guidelines. This paper presents Simple.Text, a rule-based system designed to automatically simplify all numerical expressions deemed complex, with a focus on Rules 19-25 from Section 6.2 of the Easy-to-Read guidelines. The results from the evaluation indicate a high precision and accuracy in numerical phenomena detection and transformation, although with some limitations. This system proves to be an efficient and cost-effective tool for the simplification of numerical expressions., La información numérica en los textos escritos afecta a su legibilidad y las pautas de Lectura Fácil las consideran complejas para las personas con discapacidad cognitiva. Este artículo presenta el sistema Simple.Text, un sistema basado en reglas diseñado para simplificar automáticamente todas las expresiones numéricas consideradas complejas, que aborda específicamente las reglas 19-25 de la Sección 6.2 de Lectura Fácil. Los resultados de la evaluación indican una alta precisión y exactitud en la detección de fenómenos numéricos y en su transformación, aunque con algunas limitaciones. Este sistema demuestra ser una herramienta eficiente y rentable para la simplificación de expresiones numéricas.

En este trabajo de fin de grado (TFG) se ha propuesto el diseño e implementación de un asistente virtual (chatbot) basado en procesamiento del lenguaje natural (PLN) para ofrecer información sobre el turismo en la ciudad de Alicante. Los principales temas tratados de este trabajo de fin de grado han sido: 1. Estudio exhaustivo del estado del arte sobre asistentes virtuales. 2. Revisión de herramientas, plataformas y componentes de un asistente virtual. 3. Diseño e implementación de SONIA, asistente virtual que ofrece información turística sobre la ciudad de Alicante. 4. Validación, pruebas e interpretación de resultados de SONIA. 5. Conclusiones y trabajos futuros.

The role played by science and technology parks (STPs) in technology transfer, industrial innovation, and economic growth is examined in this paper. The accurate monitoring of their evolution and impact is hindered by the lack of uniformity in STP models or goals, and the scarcity of high-quality datasets. This work uses existing terminologies, definitions, and core features of STPs to conduct a multidimensional data analysis that explores and evaluates the 21 core features which describe the key internal factors of an STP. The core features are gathered from a reliable and updatable dataset of Spanish STPs. The methodological framework can be replicated for other STP contexts and is based on descriptive techniques and machine-learning tools. The results of the study provide an overview of the general situation of STPs in Spain, validate the existence and characteristics of three types of STPs, and identify the typical features of STPs. Moreover, the prototype STP can be used as a benchmark so that other STPs can identify the features that need to be improved. Finally, this work makes it possible to carry out classifications of STPs, in addition to prediction and decision making for innovation ecosystems.

The CLEAR.TEXT project (TED2021-130707B-I00) researches how natural language processing technology can support the authoring of accessible content in Spanish for people with cognitive disabilities. Our main objective is to research, implement, deploy, evaluate, and ultimately provide robust technologies for natural language processing to support the authoring of accessible Spanish content for public sector organisations (at local, regional and national level) that is intelligible to people with cognitive disability, thereby widening their inclusion and empowerment in Europe. It is expected to impact positively the quality of life of people with cognitive disabilities, facilitating their access to educational, vocational, cultural, and social opportunities in public sector organisations.

The “Conscious Natural Text Generation” project (CORTEX, grant ref. PID2021-123956OB-I00) investigates how Natural Language Generation (NLG) architectures can be improved so that they can exploit external and commonsense knowledge, and integrate it in a controllable manner (i.e., determining what knowledge to include and how to include it) when generating new text. Our main scientific objective is, therefore, to investigate and propose novel NLG architectures that integrate different types of knowledge to automatically produce reliable, truthful and quality texts, whose information complies with the principles of objectivity and plurality, thereby minimizing bias or manipulated content. The new generation of NLG systems obtained from CORTEX will significantly improve the semantic quality of the generated text, preventing, among other phenomena, the inclusion of invented facts that do not match reality (i.e., hallucination).

In the age of knowledge, the democratisation of information facilitated through the Internet may not be as pervasive if written language poses challenges to particular sectors of the population. The objective of this paper is to present an overview of research-based automatic text simplification tools. Consequently, we describe aspects such as the language, language phenomena, language levels simplified, approaches, specific target populations these tools are created for (e.g. individuals with cognitive impairment, attention deficit, elderly people, children, language learners), and accessibility and availability considerations. The review of existing studies covering automatic text simplification tools is undergone by searching two databases: Web of Science and Scopus. The eligibility criteria involve text simplification tools with a scientific background in order to ascertain how they operate. This methodology yielded 27 text simplification tools that are further analysed. Some of the main conclusions reached with this review are the lack of resources accessible to the public, the need for customisation to foster the individual’s independence by allowing the user to select what s/he finds challenging to understand while not limiting the user’s capabilities and the need for more simplification tools in languages other than English, to mention a few.

AuTexTification is a shared task within the IberLEF workshop which aims to determine whether a text has been generated by an Artificial Intelligence (AI) or a human. The objective of this paper is to report the participation and results of the GPLSI team from the University of Alicante (Spain) in subtask 1: Human or Generated of the AuTexTification challenge for English and Spanish languages. We propose and experiment with different approaches based on Transfer Learning; Ensemble Learning; fine-tuning existing language models, such as RoBERTa or RemBERT; or relying on linguistic features. Our best models for both languages were trained through Transfer Learning techniques, obtaining the 6th and 8th position in the English and Spanish versions of this subtask, respectively. Results obtained in the Spanish-version were close to the top-performing team.

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.