Lourdes Araujo


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Approximate Nearest Neighbour Extraction Techniques and Neural Networks for Suicide Risk Prediction in the CLPsych 2022 Shared Task
Hermenegildo Fabregat Marcos | Ander Cejudo | Juan Martinez-romo | Alicia Perez | Lourdes Araujo | Nuria Lebea | Maite Oronoz | Arantza Casillas
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

This paper describes the participation of our group on the CLPsych 2022 shared task.For task A, which tries to capture changes in mood over time, we have applied an Approximate Nearest Neighbour (ANN) extraction technique with the aim of relabelling the user messages according to their proximity, based on the representation of these messages in a vector space. Regarding the subtask B, we have used the output of the subtask A to train a Recurrent Neural Network (RNN) to predict the risk of suicide at the user level.The results obtained are very competitive considering that our team was one of the few that made use of the organisers’ proposed virtual environment and also made use of the Task A output to predict the Task B results.


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NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets
Javier Cortes-Tejada | Juan Martinez-Romo | Lourdes Araujo
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

This paper describes a system for automatically classifying adverse effects mentions in tweets developed for the task 1 at Social Media Mining for Health Applications (SMM4H) Shared Task 2019. We have developed a system based on LSTM neural networks inspired by the excellent results obtained by deep learning classifiers in the last edition of this task. The network is trained along with Twitter GloVe pre-trained word embeddings.


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A Tagged Corpus for Automatic Labeling of Disabilities in Medical Scientific Papers
Carlos Valmaseda | Juan Martinez-Romo | Lourdes Araujo
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents the creation of a corpus of labeled disabilities in scientific papers. The identification of medical concepts in documents and, especially, the identification of disabilities, is a complex task mainly due to the variety of expressions that can make reference to the same problem. Currently there is not a set of documents manually annotated with disabilities with which to evaluate an automatic detection system of such concepts. This is the reason why this corpus arises, aiming to facilitate the evaluation of systems that implement an automatic annotation tool for extracting biomedical concepts such as disabilities. The result is a set of scientific papers manually annotated. For the selection of these scientific papers has been conducted a search using a list of rare diseases, since they generally have associated several disabilities of different kinds.


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Identifying Patterns for Unsupervised Grammar Induction
Jesús Santamaría | Lourdes Araujo
Proceedings of the Fourteenth Conference on Computational Natural Language Learning