New Resources and Perspectives for Biomedical Event Extraction

Event extraction is a major focus of recent work in biomedical information extraction. Despite substantial advances, many challenges still remain for reliable automatic extraction of events from text. We introduce a new biomedical event extraction resource consisting of analyses automatically created by systems participating in the recent BioNLP Shared Task (ST) 2011. In providing for the first time the outputs of a broad set of state-ofthe-art event extraction systems, this resource opens many new opportunities for studying aspects of event extraction, from the identification of common errors to the study of effective approaches to combining the strengths of systems. We demonstrate these opportunities through a multi-system analysis on three BioNLP ST 2011 main tasks, focusing on events that none of the systems can successfully extract. We further argue for new perspectives to the performance evaluation of domain event extraction systems, considering a document-level, “off-the-page ” representation and evaluation to complement the mentionlevel evaluations pursued in most recent work.

Semi-supervised method for biomedical event extraction

Introduction. In Colombia, malaria represents a serious public health problem. It is estimated that approximately 60% of the population is at risk of the disease.Objective. To describe the mortality trends for malaria in Colombia, from 1979 to 2008. Materials and methods. A descriptive study to determine the trends of the malaria mortality was carried out. The information sources used were databases of registered deaths and population projections from 1979 to 2008 of the National Statistics Department. The indicator used was the mortality rate. The trend was analyzed by join point regression.Results. Six thousands nine hundred and sixty five deaths caused by malaria were certified for an age-adjusted rate of 0.74 deaths/100.000 inhabitants for the study period. In 74.3% of the deaths, the parasite species was not mentioned. The trend in the mortality rate showed a statistically significant decreasing behavior, which was lower from the second half of the nineties as compared with that presented in the eighties.Conclusions. The magnitude of mortality by malaria in Colombia is not high, in spite of the evident underreporting. A marked downward trend was observed between 1979 and 2008. The information obtained from death certificates, along with that of the public health surveillance system will allow to modify the recommendations and improve the implementation of preventive and control measures to further reduce the mortality caused by malaria.Introducción. En Colombia, el paludismo representa un grave problema de salud pública. Se estima que, aproximadamente, 60 % de la población se encuentra en riesgo de enfermar o de morir por esta causa.Objetivo. Describir la tendencia de la mortalidad por paludismo en Colombia desde 1979 hasta 2008. Materiales y métodos. Se llevó a cabo un estudio descriptivo para determinar la tendencia de las tasas de mortalidad. Las fuentes de información fueron las bases de datos de las defunciones registradas y de las proyecciones de población de 1979 a 2008 del Departamento Nacional de Estadística (DANE). El indicador empleado fue la tasa de mortalidad. La tendencia se analizó mediante el software de análisis de regresión de puntos de inflexión (joinpoint).Resultados. Se certificaron 6.965 muertes por paludismo para una tasa ajustada por edad de 0,74 muertes por 100.000 habitantes para el periodo estudiado. En 74,3 % de las muertes, no se especificó la especie parasitaria. Las tasas de mortalidad por paludismo presentaron una tendencia decreciente estadísticamente significativa, que fue menor a partir de la segunda mitad de la década de los 90 en comparación con la presentada en la década de los 80.Conclusiones. La magnitud de la mortalidad por paludismo en Colombia no es grande, a pesar del evidente subregistro; se observó una tendencia descendente entre 1979 y 2008. La información derivada de los certificados de defunción, junto con la del sistema de vigilancia en salud pública, permitirá modificar las recomendaciones y mejorar la toma de medidas preventivas y de control pertinentes para continuar reduciendo la mortalidad causada por el paludismo

Boosting automatic event extraction from the literature using domain adaptation and coreference resolution

Motivation: In recent years, several biomedical event extraction (EE) systems have been developed. However, the nature of the annotated training corpora, as well as the training process itself, can limit the performance levels of the trained EE systems. In particular, most event-annotated corpora do not deal adequately with coreference. This impacts on the trained systems' ability to recognize biomedical entities, thus affecting their performance in extracting events accurately. Additionally, the fact that most EE systems are trained on a single annotated corpus further restricts their coverage

U-Compare bio-event meta-service: compatible BioNLP event extraction services

AbstractBackgroundBio-molecular event extraction from literature is recognized as an important task of bio text mining and, as such, many relevant systems have been developed and made available during the last decade. While such systems provide useful services individually, there is a need for a meta-service to enable comparison and ensemble of such services, offering optimal solutions for various purposes.ResultsWe have integrated nine event extraction systems in the U-Compare framework, making them inter-compatible and interoperable with other U-Compare components. The U-Compare event meta-service provides various meta-level features for comparison and ensemble of multiple event extraction systems. Experimental results show that the performance improvements achieved by the ensemble are significant. ConclusionsWhile individual event extraction systems themselves provide useful features for bio text mining, the U-Compare meta-service is expected to improve the accessibility to the individual systems, and to enable meta-level uses over multiple event extraction systems such as comparison and ensemble.This research was partially supported by KAKENHI 18002007 [YK, MM, JDK, SP, TO, JT]; JST PRESTO and KAKENHI 21500130 [YK]; the Academy of Finland and computational resources were provided by CSC -- IT Center for Science Ltd [JB, FG]; the Research Foundation Flanders (FWO) [SVL]; UK Biotechnology and Biological Sciences, Research Council (BBSRC project BB/G013160/1 Automated Biological Event Extraction from the Literature for Drug Discovery) and JISC, National Centre for Text Mining [SA]; the Spanish grant BIO2010-17527 [MN, APM]; NIH Grant U54 DA021519 [AO, DRR]Peer Reviewe