Víctor Suárez-Paniagua


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VSP at PharmaCoNER 2019: Recognition of Pharmacological Substances, Compounds and Proteins with Recurrent Neural Networks in Spanish Clinical Cases
Víctor Suárez-Paniagua
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

This paper presents the participation of the VSP team for the PharmaCoNER Tracks from the BioNLP Open Shared Task 2019. The system consists of a neural model for the Named Entity Recognition of drugs, medications and chemical entities in Spanish and the use of the Spanish Edition of SNOMED CT term search engine for the concept normalization of the recognized mentions. The neural network is implemented with two bidirectional Recurrent Neural Networks with LSTM cells that creates a feature vector for each word of the sentences in order to classify the entities. The first layer uses the characters of each word and the resulting vector is aggregated to the second layer together with its word embedding in order to create the feature vector of the word. Besides, a Conditional Random Field layer classifies the vector representation of each word in one of the mention types. The system obtains a performance of 76.29%, and 60.34% in F1 for the classification of the Named Entity Recognition task and the Concept indexing task, respectively. This method presents good results with a basic approach without using pretrained word embeddings or any hand-crafted features.


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UC3M-NII Team at SemEval-2018 Task 7: Semantic Relation Classification in Scientific Papers via Convolutional Neural Network
Víctor Suárez-Paniagua | Isabel Segura-Bedmar | Akiko Aizawa
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper reports our participation for SemEval-2018 Task 7 on extraction and classification of relationships between entities in scientific papers. Our approach is based on the use of a Convolutional Neural Network (CNN) trained on350 abstract with manually annotated entities and relations. Our hypothesis is that this deep learning model can be applied to extract and classify relations between entities for scientific papers at the same time. We use the Part-of-Speech and the distances to the target entities as part of the embedding for each word and we blind all the entities by marker names. In addition, we use sampling techniques to overcome the imbalance issues of this dataset. Our architecture obtained an F1-score of 35.4% for the relation extraction task and 18.5% for the relation classification task with a basic configuration of the one step CNN.


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LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network
Víctor Suárez-Paniagua | Isabel Segura-Bedmar | Paloma Martínez
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper, we describe our participation at the subtask of extraction of relationships between two identified keyphrases. This task can be very helpful in improving search engines for scientific articles. Our approach is based on the use of a convolutional neural network (CNN) trained on the training dataset. This deep learning model has already achieved successful results for the extraction relationships between named entities. Thus, our hypothesis is that this model can be also applied to extract relations between keyphrases. The official results of the task show that our architecture obtained an F1-score of 0.38% for Keyphrases Relation Classification. This performance is lower than the expected due to the generic preprocessing phase and the basic configuration of the CNN model, more complex architectures are proposed as future work to increase the classification rate.


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Exploring Word Embedding for Drug Name Recognition
Isabel Segura-Bedmar | Víctor Suárez-Paniagua | Paloma Martínez
Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis