Photo

Hermenegildo Fabregat

Doctoral Research @ UNED NLP Group

Madrid, Spain

gildo.fabregat (at) lsi.uned.es

Curriculum Vitæ

Github

Fields of study

Machine Learning

Natural language processing

Computer Vision

Big Data

Algorithmics

Languages

Spanish

English



Hermenegildo Fabregat is currently a post-doctoral research of computer science at the Deparment of "Lenguajes y Sistemas Informáticos (LSI)" at the National Distance Education University (UNED). He has been working as a researcher at LSI deparment at UNED since 2016.
In 2017, he obtained the MSc in Computer Science from the Complutense University of Madrid and in 2015, the degree in Computer Science from the Polytechnic University of Valencia.
His main research interest include Machine Learning and Text Mining applied to Biomedical datasets.

Education

National Distance Education University , Madrid, Spain
Current Expected 2020

Ph.D in Natural language processing

  • Thesis Title: Biomedical Information Extraction: Exploring new entities and Relationships

Advisor: Lourdes Araujo Serna

Personal website dblp

Advisor: Juan Martínez-Romo

Personal website dblp
Github
Complutense university of Madrid, Madrid, Spain
2015 - 2017

Master in Computer science

  • Thesis Titlte: Improving and Classification of biomedical images using Automatic HDL Code Generation tool
  • Advisor: Guillermo Botella Juan
  • Advisor: Alberto del Barrio
PDF

Universitat Politècnica de València, Valencia, Spain
2011 - 2015

Bachelor’s Degree in Computer Engineering

  • Thesis Titlte: Una aplicación de minería de datos para el análisis de la propiedad de terminación de SRTs
  • Advisor: Maria Jose Ramírez Quintana
  • Advisor: Javier Piris Ruano
PDF

Projects

EXTRECM Extracting Relations among Medical Concepts from Heteregenous Information Sources.

(Ministerio de Economía y Competitividad, TIN2013-46616-C2-2-R)

MAMTRA-MED Modelado, AutoMatización de exTracción de Relaciones cAtegorización de informes MEDicos para la recomendación de códigos CIE-10.

(Ministerio de Economía y Competitividad, TIN2016-77820-C3-2-R)

Publications

  • 2018, CMPB Deep neural models for extracting entities and relationships in the new RDD corpus relating disabilities and rare diseases.

    Abstract not available.

    • Hermenegildo Fabregat. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Lourdes Araujo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Juan Martinez-Romo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    Abstract Corpus Entity recognition Relation extraction
  • 2020, IEEE Access Understanding and Improving Disability Identification in Medical Documents

    Abstract not available.

    • Hermenegildo Fabregat. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Juan Martinez-Romo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Lourdes Araujo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    Abstract
  • 2019, Proces. del Leng- Natural Deep learning approach for negation trigger and scope recognition

    Abstract not available.

    • Hermenegildo Fabregat. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Lourdes Araujo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Juan Martinez-Romo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    Abstract

Conferences

  • 2020, CIRCLE Anorexia Topical Trends in Self-declared Reddit Users

    Social Media platforms have been a vital environment to share experiences and seek knowledge. People with various interests form online communities in which they can accumulate many experiences from many peers. Among these communities are the mental health-related ones that have been growing on Social Media in the last few years. However, users can show alarming behavioral signs at the stage of their mental illness that should be identified before it is too late. Hence, equipping social media platforms with the needed tools to monitor its users, identify risks, and intervene on time has been of great concern recently. In this paper, we target users who self disclose as being diagnosed with an eating disorder, namely Anorexia. We provide a dataset of manually labeled Reddit users’ posts, focused on the extraction of some potentially relevant topics for the study of eating disorders. E.g. diets, exercises, body image, etc. These topics can be utilized to find patterns in Anorexic users’ behaviors to distinguish them from users who are less likely to have Anorexia. They can also be used to interpret afflicted users’ attitudes. We support our labeling with baseline experiments to learn how to differentiate between these topics.

    • Razan Masood. Department of Computer Science and Applied Cognitive Science, University of Duisburg–Essen, Germany
    • Mengjiao Hu. University of Duisburg-Essen, Duisburg, Germany
    • Hermenegildo Fabregat. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Ahmet Aker. Department of Computer Science and Applied Cognitive Science, University of Duisburg–Essen, Germany
    • Norbert Fuhr. Department of Computer Science and Applied Cognitive Science, University of Duisburg–Essen, Germany
    Report
  • 2017, Summer Sim Simulation and implementation of a low-cost platform to improve the quality of biological images.

    The development of fluorescence microscopy techniques has helped advance in the understanding of molecular mechanisms present at biological systems, as the case of cell migration (Castillo-Lluva et al. 2010, Castillo-Lluva et al. 2013). These techniques allow studying the location, distribution as well as the movement of molecules that have been fluorescently tagged at a high resolution, even dealing with live cells (Rieckher 2017). In this paper a filtering and pattern recognition flow is simulated and implemented on a low-cost hardware platform in order to automate the detection of circular and ellipsoidal cells within the microscopic images. Results show that images keep a high quality after filtering and that 78.4% of the objects are properly recognized.

    • Guillermo Botella. Department of Computer Architecture and Automation, Complutense University of Madrid.
    • H. Fabregat. Complutense University of Madrid.
    • Alberto A. Del Barrio. Department of Computer Architecture and Automation, Complutense University of Madrid.
    Report
  • 2015, PROLE Analysing the Termination of Term Rewriting Systems using Data Mining.

    During the last decades, researchers in the field of Term Rewriting System (TRS) have devoted a lot of effort in order to develop techniques and methods able to demonstrate the termination property of a TRS. As a consequence, some of the proposed techniques have been implemented and several termination tools have been developed in order to automatize the termination proofs. From 2004, the annual Termination Competition is the forum in which research groups compare their tools trying to provide termination proofs of as many TRS as possible. This event generates a large amount of information (results obtained by the different tools, time spent on each proof, …) that is recorded in databases. In this paper, we propose an alternative approach to study the termination of TRS: to use data mining techniques that, based on the historical information collected in the competition, generate models to explore the termination of a TRS. The goal of our study is not to develop a termination tool but to show, for the first time, what machine learning techniques can offer to the analysis of TRS termination.

    • J. Piris. DSIC, Universitat Politecnica de Valéncia
    • H. Fabregat. DSIC, Universitat Politecnica de Valéncia
    • M.J. Ramírez-Quintana. DSIC, Universitat Politecnica de Valéncia
    Report

Workshops

  • 2021, eHealth@CLEF LSI_UNED at CLEF eHealth2021: Exploring the effects of transfer learning in negation detection and entity recognition in clinical texts

    Abstract not available.

    • Hermenegildo Fabregat. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Andres Duque. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Lourdes Araujo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Juan Martinez-Romo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    Proceeding
  • 2021, eRisk@CLEF NLP-UNED at eRisk 2021: self-harm early risk detection with TF-IDF and linguistic features.

    Abstract not available.

    • Elena Campillo Ageitos. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Hermenegildo Fabregat. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Lourdes Araujo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Juan Martinez-Romo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    Proceeding
  • 2019, eHealth@IberLEF NLP_UNED at eHealth-KD Challenge 2019: Deep Learning for Named Entity Recognition and Attentive Relation Extraction

    Abstract not available.

    • Hermenegildo Fabregat. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Andres Duque. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Juan Martinez-Romo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Lourdes Araujo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    Proceeding
  • 2019, NEGES@IberLEF Extending a Deep Learning Approach for Negation Cues Detection in Spanish.

    Abstract not available.

    • Hermenegildo Fabregat. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Andres Duque. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Juan Martinez-Romo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Lourdes Araujo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    Proceeding
  • 2019, IberLEF De-Identification through Named Entity Recognition for Medical Document Anonymization

    Abstract not available.

    • Hermenegildo Fabregat. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Andres Duque. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Juan Martinez-Romo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Lourdes Araujo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    Proceeding
  • 2018, NEGES Deep Learning approach for Negation Cues Detection in Spanish

    Abstract not available.

    • Hermenegildo Fabregat. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Lourdes Araujo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Juan Martinez-Romo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    Proceeding
  • 2018, DIANN Overview of the DIANN Task: Disability Annotation Task

    Abstract not available.

    • Hermenegildo Fabregat. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Lourdes Araujo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Juan Martinez-Romo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    Proceeding Github
  • 2018, DIANN UNED at DIANN 2018: Unsupervised System for Automatic Disabilities Labeling in Medical Scientific Documents

    Abstract not available.

    • Hermenegildo Fabregat. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Lourdes Araujo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    • Juan Martinez-Romo. Department of Computer Science, Universidad Nacional de Educación a Distancia (UNED)
    Proceeding