Isabel Segura-Bedmar

Also published as: Isabel Segura Bedmar


2018

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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.

2017

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Exploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets
Isabel Segura-Bedmar | Antonio Quirós | Paloma Martínez
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Spanish is the third-most used language on the internet, after English and Chinese, with a total of 7.7% (more than 277 million of users) and a huge internet growth of more than 1,400%. However, most work on sentiment analysis has been focused on English. This paper describes a deep learning system for Spanish sentiment analysis. To the best of our knowledge, this is the first work that explores the use of a convolutional neural network to polarity classification of Spanish tweets.

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LABDA at SemEval-2017 Task 10: Extracting Keyphrases from Scientific Publications by combining the BANNER tool and the UMLS Semantic Network
Isabel Segura-Bedmar | Cristóbal Colón-Ruiz | Paloma Martínez
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the system presented by the LABDA group at SemEval 2017 Task 10 ScienceIE, specifically for the subtasks of identification and classification of keyphrases from scientific articles. For the task of identification, we use the BANNER tool, a named entity recognition system, which is based on conditional random fields (CRF) and has obtained successful results in the biomedical domain. To classify keyphrases, we study the UMLS semantic network and propose a possible linking between the keyphrase types and the UMLS semantic groups. Based on this semantic linking, we create a dictionary for each keyphrase type. Then, a feature indicating if a token is found in one of these dictionaries is incorporated to feature set used by the BANNER tool. The final results on the test dataset show that our system still needs to be improved, but the conditional random fields and, consequently, the BANNER system can be used as a first approximation to identify and classify keyphrases.

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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.

2016

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LABDA at the 2016 BioASQ challenge task 4a: Semantic Indexing by using ElasticSearch
Isabel Segura-Bedmar | Adrián Carruana | Paloma Martínez
Proceedings of the Fourth BioASQ workshop

2015

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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

2014

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Detecting drugs and adverse events from Spanish social media streams
Isabel Segura-Bedmar | Ricardo Revert | Paloma Martínez
Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

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Extracting drug indications and adverse drug reactions from Spanish health social media
Isabel Segura-Bedmar | Santiago de la Peña González | Paloma Martínez
Proceedings of BioNLP 2014

2013

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SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013)
Isabel Segura-Bedmar | Paloma Martínez | María Herrero-Zazo
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2008

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A preliminary approach to extract drugs by combining UMLS resources and USAN naming conventions
Isabel Segura-Bedmar | Paloma Martínez | Doaa Samy
Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing

2007

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UCM3: Classification of Semantic Relations between Nominals using Sequential Minimal Optimization
Isabel Segura Bedmar | Doaa Samy | Jose L. Martinez
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)