Manuel Jesús Maña López
University of Huelva
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Featured researches published by Manuel Jesús Maña López.
empirical methods in natural language processing | 2015
Noa Patricia Cruz Díaz; Manuel Jesús Maña López
Choosing the right tokenizer is a non-trivial task, especially in the biomedical domain, where it poses additional challenges, which if not resolved means the propagation of errors in successive Natural Language Processing analysis pipeline. This paper aims to identify these problematic cases and analyze the output that, a representative and widely used set of tokenizers, shows on them. This work will aid the decision making process of choosing the right strategy according to the downstream application. In addition, it will help developers to create accurate tokenization tools or improve the existing ones. A total of 14 problematic cases were described, showing biomedical samples for each of them. The outputs of 12 tokenizers were provided and discussed in relation to the level of agreement among tools.
Proceedings of the Workshop Computational Semantics Beyond Events and Roles | 2017
Noa Cruz; Roser Morante; Manuel Jesús Maña López; Jacinto Mata Vázquez; Carlos L. Parra Calderón
In this paper we present on-going work on annotating negation in Spanish clinical documents. A corpus of anamnesis and radiology reports has been annotated by two domain expert annotators with negation markers and negated events. The Dice coefficient for inter-annotator agreement is higher than 0.94 for negation markers and higher than 0.72 for negated events. The corpus will be publicly released when the annotation process is finished, constituting the first corpus annotated with negation for Spanish clinical reports available for the NLP community.
Journal of the American Medical Informatics Association | 2013
Mariano Crespo Azcárate; Jacinto Mata Vázquez; Manuel Jesús Maña López
Objective We explored two strategies for query expansion utilizing medical subject headings (MeSH) ontology to improve the effectiveness of medical image retrieval systems. In order to achieve greater effectiveness in the expansion, the search text was analyzed to identify which terms were most amenable to being expanded. Design To perform the expansions we utilized the hierarchical structure by which the MeSH descriptors are organized. Two strategies for selecting the terms to be expanded in each query were studied. The first consisted of identifying the medical concepts using the unified medical language system metathesaurus. In the second strategy the text of the query was divided into n-grams, resulting in sequences corresponding to MeSH descriptors. Measurements For the evaluation of the system, we used the collection made available by the ImageCLEF organization in its 2011 medical image retrieval task. The main measure of efficiency employed for evaluating the techniques developed was the mean average precision (MAP). Results Both strategies exceeded the average MAP score in the ImageCLEF 2011 competition (0.1644). The n-gram expansion strategy achieved a MAP of 0.2004, which represents an improvement of 21.89% over the average MAP score in the competition. On the other hand, the medical concepts expansion strategy scored 0.2172 in the MAP, representing a 32.11% improvement. This run won the text-based medical image retrieval task in 2011. Conclusions Query expansion exploiting the hierarchical structure of the MeSH descriptors achieved a significant improvement in image retrieval systems.OBJECTIVE We explored two strategies for query expansion utilizing medical subject headings (MeSH) ontology to improve the effectiveness of medical image retrieval systems. In order to achieve greater effectiveness in the expansion, the search text was analyzed to identify which terms were most amenable to being expanded. DESIGN To perform the expansions we utilized the hierarchical structure by which the MeSH descriptors are organized. Two strategies for selecting the terms to be expanded in each query were studied. The first consisted of identifying the medical concepts using the unified medical language system metathesaurus. In the second strategy the text of the query was divided into n-grams, resulting in sequences corresponding to MeSH descriptors. MEASUREMENTS For the evaluation of the system, we used the collection made available by the ImageCLEF organization in its 2011 medical image retrieval task. The main measure of efficiency employed for evaluating the techniques developed was the mean average precision (MAP). RESULTS Both strategies exceeded the average MAP score in the ImageCLEF 2011 competition (0.1644). The n-gram expansion strategy achieved a MAP of 0.2004, which represents an improvement of 21.89% over the average MAP score in the competition. On the other hand, the medical concepts expansion strategy scored 0.2172 in the MAP, representing a 32.11% improvement. This run won the text-based medical image retrieval task in 2011. CONCLUSIONS Query expansion exploiting the hierarchical structure of the MeSH descriptors achieved a significant improvement in image retrieval systems.
Archive | 2007
Francisco Carrero García; Enrique Puertas Sanz; José María Gómez Hidalgo; Manuel Jesús Maña López; Jacinto Mata
Procesamiento Del Lenguaje Natural | 2001
Ignacio Acero; Matías Alcojor; Alberto Díaz Esteban; José María Gómez Hidalgo; Manuel Jesús Maña López
text retrieval conference | 2011
Juan Manuel Córdoba; Manuel Jesús Maña López; Noa Patricia Cruz Díaz; Jacinto Mata Vázquez; Fernando Aparicio; Manuel de Buenaga Rodríguez; Daniel Glez-Peña; Florentino Fdez-Riverola
Procesamiento Del Lenguaje Natural | 2011
Manuel de la Villa Cordero; Sebastián García Pérez; Manuel Jesús Maña López
Procesamiento Del Lenguaje Natural | 2007
Francisco Carrero García; José María Gómez Hidalgo; Manuel de Buenaga Rodríguez; Jacinto Mata Vázquez; Manuel Jesús Maña López
Procesamiento Del Lenguaje Natural | 2011
Jacinto Mata Vázquez; Mariano Crespo; Manuel Jesús Maña López
Procesamiento Del Lenguaje Natural | 2010
Noa Patricia Cruz Díaz; Manuel Jesús Maña López; Jacinto Mata Vázquez