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This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt has been designed to provide a flexible framework where a researcher can define the space of possible pipelines to solve a specific machine learning problem, which can range from high-level decisions about representation and features to low-level hyper-parameter values. The evaluation of HML-Opt is presented via two different case studies, both of which demonstrate that it is competitive with existing AutoML tools on a variety of benchmarks. Furthermore, HML-Opt can be applied to novel problems, such as knowledge extraction from natural language text, whereas other techniques are insufficiently flexible to capture the complexity of these scenarios. The source code for HML-Opt is available online for the research community. ; This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University of Havana. Moreover, it has also been partially funded by both aforementioned universities, the Generalitat Valenciana (Conselleria d'Educació, Investigació, Cultura i Esport) and the Spanish Government through the projects LIVING-LANG (RTI2018-094653-B-C22) and SIIA (PROMETEO/2018/089, PROMETEU/2018/089).

The massive amount of biomedical information published online requires the development of automatic knowledge discovery technologies to effectively make use of this available content. To foster and support this, the research community creates linguistic resources, such as annotated corpora, and designs shared evaluation campaigns and academic competitive challenges. This work describes an ecosystem that facilitates research and development in knowledge discovery in the biomedical domain, specifically in Spanish language. To this end, several resources are developed and shared with the research community, including a novel semantic annotation model, an annotated corpus of 1045 sentences, and computational resources to build and evaluate automatic knowledge discovery techniques. Furthermore, a research task is defined with objective evaluation criteria, and an online evaluation environment is setup and maintained, enabling researchers interested in this task to obtain immediate feedback and compare their results with the state-of-the-art. As a case study, we analyze the results of a competitive challenge based on these resources and provide guidelines for future research. The constructed ecosystem provides an effective learning and evaluation environment to encourage research in knowledge discovery in Spanish biomedical documents. ; This research has been partially supported by the University of Alicante and University of Havana, the Generalitat Valenciana (Conselleria d'Educació, Investigació, Cultura i Esport) and the Spanish Government through the projects SIIA (PROMETEO/2018/089, PROMETEU/2018/089) and LIVING-LANG (RTI2018-094653-B-C22).

This paper presents AutoGOAL, a system for automatic machine learning (AutoML) that uses heterogeneous techniques. In contrast with existing AutoML approaches, our contribution can automatically build machine learning pipelines that combine techniques and algorithms from different frameworks, including shallow classifiers, natural language processing tools, and neural networks. We define the heterogeneous AutoML optimization problem as the search for the best sequence of algorithms that transforms specific input data into the desired output. This provides a novel theoretical and practical approach to AutoML. Our proposal is experimentally evaluated in diverse machine learning problems and compared with alternative approaches, showing that it is competitive with other AutoML alternatives in standard benchmarks. Furthermore, it can be applied to novel scenarios, such as several NLP tasks, where existing alternatives cannot be directly deployed. The system is freely available and includes in-built compatibility with a large number of popular machine learning frameworks, which makes our approach useful for solving practical problems with relative ease and effort. ; This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University of Havana. Moreover, it has also been partially funded by both aforementioned universities, the Generalitat Valenciana (Conselleria d'Educació, Investigació, Cultura i Esport) and the Spanish Government through the projects LIVING-LANG (RTI2018-094653-B-C22) and SIIA (PROMETEO/2018/089, PROMETEU/2018/089).

Corpora are one of the most valuable resources at present for building machine learning systems. However, building new corpora is an expensive task, which makes the automatic extension of corpora a highly attractive task to develop. Hence, finding new strategies that reduce the cost and effort involved in this task, while at the same time guaranteeing quality, remains an open and important challenge for the research community. In this paper, we present a set of ensembling strategies oriented toward entity and relation extraction tasks. The main goal is to combine several automatically annotated versions of corpora to produce a single version with improved quality. An ensembler is built by exploring a configuration space in search of the combination that maximizes the fitness of the ensembled collection according to a reference collection. The eHealth-KD 2019 challenge was chosen for the case study. The submitted systems' outputs were ensembled, resulting in the construction of an automatically annotated collection of 8000 sentences. We show that using this collection as additional training input for a baseline algorithm has a positive impact on its performance. Additionally, the ensembling pipeline was used as a participant system in the 2020 edition of the challenge. The ensembled run achieved a slightly better performance than the individual runs. ; This research has been partially funded by the University of Alicante and the University of Havana, the Generalitat Valenciana (Conselleria d'Educació, Investigació, Cultura i Esport) and the Spanish Government through the projects LIVING-LANG (RTI2018-094653-B-C22) and SIIA (PROMETEO/2018/089, PROMETEU/2018/089). Moreover, it has been backed by the work of both COST Actions: CA19134 - "Distributed Knowledge Graphs" and CA19142 - "Leading Platform for European Citizens, Industries, Academia and Policymakers in Media Accessibility".

This paper summarises the eHealth Knowledge Discovery Challenge hosted at IberLEF 2021. We describe the task, resources, and participating systems, highlighting and discussing the main results achieved in the challenge. We analyse the best performing systems and present recommendations for future research. ; Este artículo resume la Tarea de Descubrimiento de Conocimiento en Salud presentada en IberLEF 2021. Se describen la tarea, los recursos creados, y los sistemas que participaron. Se discuten los resultados principales obtenidos por estos sistemas, y se presentan recomendaciones para continuar la investigación en esta temática. ; This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University of Havana. Moreover, the research has been partially funded by the University of Alicante and the University of Havana, the Generalitat Valenciana (Conselleria d'Educació, Investigació, Cultura i Esport) and the Spanish Government through the projects LIVING-LANG (RTI2018-094653-B-C22), INTEGER (RTI2018-094649-B-I00) and SIIA (PROMETEO/2018/089, PROMETEU/2018/089). Additionally, it has been backed by the work of both COST Actions: CA19134 – "Distributed Knowledge Graphs" and CA19142 – "Leading Platform for European Citizens, Industries, Academia and Policymakers in Media Accessibility".

This paper introduces a web demo that showcases the main characteristics of the AutoGOAL framework. AutoGOAL is a framework in Python for automatically finding the best way to solve a given task. It has been designed mainly for automatic machine learning (AutoML) but it can be used in any scenario where several possible strategies are available to solve a given computational task. In contrast with alternative frameworks, AutoGOAL can be applied seamlessly to Natural Language Processing as well as structured classification problems. This paper presents an overview of the framework's design and experimental evaluation in several machine learning problems, including two recent NLP challenges. The accompanying software demo is available online and full source code is provided under the MIT open-source license. ; This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University of Havana. Moreover, it has also been partially funded by both aforementioned universities, the Generalitat Valenciana (Conselleria d'Educaci´o, Investigaci´o, Cultura i Esport) and the Spanish Government through the projects LIVING-LANG (RTI2018-094653-B-C22) and SIIA (PROMETEO/2018/089, PROMETEU/2018/089).

El aprendizaje automático es un campo de la inteligencia artificial que ha ganado un reciente interés en todas las áreas de la industria, motivado fundamentalmente por el acelerado crecimiento de las capacidades de cómputo y la disponibilidad de datos. Sin embargo, una de las principales dificultades para su aplicación es la necesidad de expertos que conozcan los detalles internos de los múltiples modelos que pueden ser utilizados. En este contexto ha surgido un nuevo campo de estudio, denominado AutoML (Automated Machine Learning), que facilita la utilización de estas técnicas por expertos de otros dominios. Este artículo presenta una propuesta concreta de un sistema —AutoGOAL— que ha sido diseñada para resolver problemas de aprendizaje automático de variada naturaleza. Además, se realiza una breve comparación entre sistemas existentes de relevancia en el campo. La propuesta es competitiva con herramientas del estado del arte en problemas clásicos de aprendizaje, a la vez que puede desplegarse, sin esfuerzo adicional, en dominios más complejos, como el procesamiento de lenguaje natural. AutoGOAL constituye un paso más hacia la democratización del aprendizaje automático para usuarios no expertos en el tema. ; Machine Learning is a field of Artificial Intelligence that has gained recent interest in all areas of the industry, motivated primarily by the accelerated growth of computer capabilities and data availability. However, one of the main difficulties for its application is the need for experts who know the internal details of the multiple models that can be used. In this context, a new field of study has emerged, AutoML (Automated Machine Learning), which facilitates the use of these techniques by experts from other domains. This paper presents a concrete proposal of a system —AutoGOAL— which has been designed to solve machine learning problems of various kinds. In addition, a brief comparison is made between relevant existing systems in the field. The proposal is competitive with state-of-the-art tools in classic machine learning problems, and it can be seamlessly deployed in more complex domains, such as natural language processing. AutoGOAL is another step towards the democratization of machine learning for non-expert users.

Machine Learning is a field of Artificial Intelligence that has gained recent interest in all areas of the industry, motivated primarily by the accelerated growth of computer capabilities and data availability. However, one of the main difficulties for its application is the need for experts who know the internal details of the multiple models that can be used. In this context, a new field of study has emerged, AutoML(Automated Machine Learning), which facilitates the use of these techniques by experts from other domains. This paper presents an introduction to the field of AutoML, a brief comparison between existing tools, and a concrete proposal of a technology —AutoGOAL, own authorship— which has been designed to solve machine learning problems of various kinds. Our proposal is competitive with state-of-the-art tools in classic machine learning problems, and it can be seamlessly deployed in more complex domains, such as natural language processing. ; El Aprendizaje Automático es un campo de la Inteligencia Artificial que ha ganado un reciente interés en todas las áreas de la industria, motivado fundamentalmente por el acelerado crecimiento de las capacidades de cómputo y la disponibilidad de datos. Sin embargo, una de las principales dificultades para su aplicación es la necesidad de expertos que conozcan los detalles internos de los múltiples modelos que pueden ser utilizados. En este contexto ha surgido un nuevo campo de estudio, denominado AutoML(Automated Machine Learning), que facilita la utilización de estas técnicas por expertos de otros dominios. Este artículo presenta una introducción al campo del AutoML, una breve comparación entre las herramientas existentes, y una propuesta concreta de una tecnología —AutoGOAL— que ha sido diseñada para resolver problemas de aprendizaje automático de variada naturaleza. Nuestra propuesta es competitiva con herramientas del estado del arte en problemas clásicos de aprendizaje, a la vez que puede desplegarse, sin esfuerzo adicional, en dominios más complejos, como el ...

Machine Learning is a field of Artificial Intelligence that has gained recent interest in all areas of the industry, motivated primarily by the accelerated growth of computer capabilities and data availability. However, one of the main difficulties for its application is the need for experts who know the internal details of the multiple models that can be used. In this context, a new field of study has emerged, AutoML(Automated Machine Learning), which facilitates the use of these techniques by experts from other domains. This paper presents an introduction to the field of AutoML, a brief comparison between existing tools, and a concrete proposal of a technology —AutoGOAL, own authorship— which has been designed to solve machine learning problems of various kinds. Our proposal is competitive with state-of-the-art tools in classic machine learning problems, and it can be seamlessly deployed in more complex domains, such as natural language processing. ; El Aprendizaje Automático es un campo de la Inteligencia Artificial que ha ganado un reciente interés en todas las áreas de la industria, motivado fundamentalmente por el acelerado crecimiento de las capacidades de cómputo y la disponibilidad de datos. Sin embargo, una de las principales dificultades para su aplicación es la necesidad de expertos que conozcan los detalles internos de los múltiples modelos que pueden ser utilizados. En este contexto ha surgido un nuevo campo de estudio, denominado AutoML(Automated Machine Learning), que facilita la utilización de estas técnicas por expertos de otros dominios. Este artículo presenta una introducción al campo del AutoML, una breve comparación entre las herramientas existentes, y una propuesta concreta de una tecnología —AutoGOAL— que ha sido diseñada para resolver problemas de aprendizaje automático de variada naturaleza. Nuestra propuesta es competitiva con herramientas del estado del arte en problemas clásicos de aprendizaje, a la vez que puede desplegarse, sin esfuerzo adicional, en dominios más complejos, como el procesamiento de lenguaje natural.

This paper summarises the results of the third edition of the eHealth Knowledge Discovery (KD) challenge, hosted at the Iberian Language Evaluation Forum 2020. The eHealth-KD challenge proposes two computational tasks involving the identification of semantic entities and relations in natural language text, focusing on Spanish language health documents. In this edition, besides text extracted from medical sources, Wikipedia content was introduced into the corpus, and a novel transfer-learning evaluation scenario was designed that challenges participants to create systems that provide cross-domain generalisation. A total of eight teams participated with a variety of approaches including deep learning end-to-end systems as well as rule-based and knowledge-driven techniques. This paper analyses the most successful approaches and highlights the most interesting challenges for future research in this field. ; This research has been partially supported by the University of Alicante and University of Havana, the Generalitat Valenciana (Conselleria d'Educació, Investigació, Cultura i Esport) and the Spanish Government through the projects SIIA (PROMETEO/2018/089, PROMETEU/2018/089) and LIVING-LANG (RTI2018-094653-B-C22).

This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of 500 sentences that were manually selected by the researchers from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of 100,959 sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents. ; This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University of Havana. Moreover, it has also been partially funded by both aforementioned universities, the Generalitat Valenciana (Conselleria d'Educació, Investigació, Cultura i Esport) and the Spanish Government through the projects LIVING-LANG (RTI2018-094653-B-C22) and SIIA (PROMETEO/2018/089).

Cuba's National Health System has managed to guarantee an effective and equitable response to COVID-19. Universal and free health coverage, based on primary care, follows the principle of equity and the greatest resources are allocated to areas of the lowest socioeconomic stratum (where higher risk is concentrated), followed by those of medium and high strata, in that order. This allowed for similar mortality rates in the three strata, and Cuban national mortality rate was one of the lowest in the Region of the Americas. Before the first case was identified in Cuba, a Plan for Coronavirus Prevention and Control was elaborated with multisectoral participation, and when the first case was confirmed the Temporary National Working Group to Fight COVID-19 was created as an advisory body of the government. The actions to face the pandemic began with preventive measures in the community, continued in the isolation centers and ended again in the community with actions of surveillance and follow up of recovered patients. Following the principle of territoriality, molecular diagnosis laboratories were created in the provinces that did not have one. Free medical care and treatment; the preparation of a single national intersectoral government plan; the use of particular strategies for research, diagnosis and case tracing; and the implementation of a universal protocol for disease prevention and treatment of confirmed cases made it possible to control the disease with a health equity perspective.

Cuba's National Health System has managed to guarantee an effective and equitable response to COVID-19. Universal and free health coverage, based on primary care, follows the principle of equity, and the greatest resources are allocated to areas of the lowest socioeconomic stratum (which concentrates the higher health risks), followed by those of medium and high strata, in that order. This allowed for similar mortality rates in the three strata, and Cuban national mortality rate was one of the lowest in the Region of the Americas. Before the first case was identified in Cuba, a Plan for Coronavirus Prevention and Control was elaborated with multisectoral participation, and when the first case was confirmed the Temporary Working Group to Fight COVID-19 was created as an advisory body of the government. The actions to face the pandemic began with preventive measures in the community, continued in the isolation centers and ended again in the community with actions of surveillance and follow up of recovered patients. Following the principle of territoriality, laboratories of molecular diagnosis were created in the provinces that did not have it. Free medical care and treatment; the preparation of a single national intersectoral government plan; the use of particular strategies for research, diagnosis and case tracing; and the implementation of a universal protocol for disease prevention and treatment of confirmed cases allowed to control the disease with a perspective of equity in health.

This is an overview of the Workshop on Semantic Analysis at the SEPLN congress held in Sevilla, Spain, in September 2018. This forum proposes to participants four different semantic tasks on texts written in Spanish. Task 1 focuses on polarity classification; Task 2 encourages the development of aspect-based polarity classification systems; Task 3 provides a scenario for discovering knowledge from eHealth documents; finally, Task 4 is about automatic classification of news articles according to safety. The former two tasks are novel in this TASS's edition. We detail the approaches and the results of the submitted systems of the different groups in each task. ; Este artículo ofrece un resumen sobre el Taller de Análisis Semántico en la SEPLN (TASS) celebrado en Sevilla, España, en septiembre de 2018. Este foro propone a los participantes cuatro tareas diferentes de análisis semántico sobre textos en español. La Tarea 1 se centra en la clasificación de la polaridad; la Tarea 2 anima al desarrollo de sistemas de polaridad orientados a aspectos; la Tarea 3 consiste en descubrir conocimiento en documentos sobre salud; finalmente, la Tarea 4 propone la clasificación automática de noticias periodísticas según un nivel de seguridad. Las dos últimas tareas son nuevas en esta edición. Se ofrece una síntesis de los sistemas y los resultados aportados por los distintos equipos participantes, así como una discusión sobre los mismos. ; This work has been partially supported by a grant from the Fondo Europeo de Desarrollo Regional (FEDER), the projects REDES (TIN2015-65136-C2-1-R, TIN2015-65136-C2-2-R) and SMART-DASCI (TIN2017-89517-P) from the Spanish Government, and "Plataforma Inteligente para Recuperación, Análisis y Representación de la Información Generada por Usuarios en Internet" (GRE16-01) from University of Alicante. Eugenio Martínez Cámara was supported by the Spanish Government Programme Juan de la Cierva Formación (FJCI-2016-28353).