Manuel Carlos Díaz Galiano


2019

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Using Snomed to recognize and index chemical and drug mentions.
Pilar López Úbeda | Manuel Carlos Díaz Galiano | L. Alfonso Urena Lopez | Maite Martin
Proceedings of The 5th Workshop on BioNLP Open Shared Tasks

In this paper we describe a new named entity extraction system. Our work proposes a system for the identification and annotation of drug names in Spanish biomedical texts based on machine learning and deep learning models. Subsequently, a standardized code using Snomed is assigned to these drugs, for this purpose, Natural Language Processing tools and techniques have been used, and a dictionary of different sources of information has been built. The results are promising, we obtain 78% in F1 score on the first sub-track and in the second task we map with Snomed correctly 72% of the found entities.

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Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media
Pilar López Úbeda | Manuel Carlos Díaz Galiano | Maite Martin | L. Alfonso Urena Lopez
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

Over time the use of social networks is becoming very popular platforms for sharing health related information. Social Media Mining for Health Applications (SMM4H) provides tasks such as those described in this document to help manage information in the health domain. This document shows the first participation of the SINAI group. We study approaches based on machine learning and deep learning to extract adverse drug reaction mentions from Twitter. The results obtained in the tasks are encouraging, we are close to the average of all participants and even above in some cases.

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Detecting Anorexia in Spanish Tweets
Pilar López Úbeda | Flor Miriam Plaza del Arco | Manuel Carlos Díaz Galiano | L. Alfonso Urena Lopez | Maite Martin
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Mental health is one of the main concerns of today’s society. Early detection of symptoms can greatly help people with mental disorders. People are using social networks more and more to express emotions, sentiments and mental states. Thus, the treatment of this information using NLP technologies can be applied to the automatic detection of mental problems such as eating disorders. However, the first step to solving the problem should be to provide a corpus in order to evaluate our systems. In this paper, we specifically focus on detecting anorexia messages on Twitter. Firstly, we have generated a new corpus of tweets extracted from different accounts including anorexia and non-anorexia messages in Spanish. The corpus is called SAD: Spanish Anorexia Detection corpus. In order to validate the effectiveness of the SAD corpus, we also propose several machine learning approaches for automatically detecting anorexia symptoms in the corpus. The good results obtained show that the application of textual classification methods is a promising option for developing this kind of system demonstrating that these tools could be used by professionals to help in the early detection of mental problems.