Article "Lexical Simplification System to Improve Web Accessibility" published in the journal IEEE Access.

Arquitectura sistema EASIER People with intellectual, language and learning disabilities face accessibility barriers when reading texts with complex words. To offer support to these reading aids, a lexical simplification system for Spanish has been developed and is presented in this article. The system covers the complex word identification task and offers replacement candidates with the substitute generation and selection task. These tasks have followed machine learning techniques and contextual embeddings using Easy Reading and Plain Language resources, such as dictionaries and corpora. These findings represent an additional advancement in the lexical simplification of texts in Spanish and in a generic domain using easy-to-read resources. The article is by Rodrigo Alarcon, Lourdes Moreno and Paloma Martínez from the HULAT group in the EASIER project.

"Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals" paper published in Frontiers in Neuroinformatics journal

Entrevista de radio en "Hoy por Hoy" de la SER, explicando el proyecto EASIER, 

Icono de programa Hoy por Hoy Madrid, SER Entrevista de radio en "Hoy por Hoy" de la SER a Lourdes Moreno del grupo HULAT por el proyecto EASIER, un sistema de simplificación léxica en español para hacer la información accesible a las personas con discapacidad intelectual financiado por INDRA y Fundación Universa. En este proyecto se han utilizado técnicas de accesibilidad y de Procesamiento de Lenguaje Natural.

"An IoT-based contribution to improve mobility of the visually impaired in Smart Cities" paper published in Computing journal (Springer)

Plataforma EASIER, una ayuda en la comprensión de los textos

Logotipo Plataforma EASIER EASIER es una plataforma que ayuda a las personas a comprender mejor los textos y funciona apoyándose en métodos de inteligencia artificial. Básicamente proporciona simplificación léxica de los textos en español ofreciendo distintas ayudas a la comprensión.

"Deep-Sync: A novel deep learning-based tool for semantic-aware subtitling synchronisation" paper published in "Neural Computing and Applications" journal

In this paper, we present Deep-Sync, a tool for the alignment of subtitles with the audio-visual content. The architecture integrates a deep language representation model and a real-time voice recognition software to build a semantic-aware alignment tool that successfully aligns most of the subtitles even when there is no direct correspondence between the re-speaker and the audio content. Deep-Sync was compared with other subtitles alignment tool, showing that our proposal is able to improve the synchronisation in all tested cases.

“Disambiguating Clinical Abbreviations Using Pretrained Word Embeddings” accepted in HEALTHINF 2021 conference

poster healthinf 2021 The paper “Disambiguating Clinical Abbreviations Using Pretrained Word Embeddings” has been accepted in HEALTHINF 2021 conference

Participation of HULAT in SDU@AAAI-21: A Hybrid Approach to Disambiguate Scientific Acronyms

Approach to disambiguate acronyms Poster Participation of HULAT in SDU@AAAI-21 shared task for Acronym Disambiguation. The work will be presented inAAAI-21 Workshop on Scientific Document Understanding, February 9 2021

Seminario de investigación: "Automatic Text Simplification and Summarization" (18-22 enero 2021)

Horacio Saggion, profesor de la Universidad Pompeu Fabra, será el ponente del seminario "Automatic Text Simplification and Summarization" que se impartirá on line del 18 al 22 de enero de 2021 en el marco del Master de Ciencia y Tecnología de la Universidad Carlos III de Madrid. Se abordarán las tareas de simplificación de textos y generación automática de resúmenes como dos tareas de procesamiento del lenguaje natural, exponiendo métodos, recursos, arquitecturas y evaluación de sistemas.

Published Paper "Automatic Learning Framework for Pharmaceutical Record Matching"

This article presents a framework for pharmaceutical record matching based on machine learning techniques in a big data environment. Available in open access here.