Incorporating rich background knowledge for gene named entity classification and recognition

<p>Abstract</p> <p>Background</p> <p>Gene named entity classification and recognition are crucial preliminary steps of text mining in biomedical literature. Machine learning based methods have been used in this area with great success. In most state-of-the-art systems, elaborately designed lexical features, such as words, n-grams, and morphology patterns, have played a central part. However, this type of feature tends to cause extreme sparseness in feature space. As a result, out-of-vocabulary (OOV) terms in the training data are not modeled well due to lack of information.</p> <p>Results</p> <p>We propose a general framework for gene named entity representation, called feature coupling generalization (FCG). The basic idea is to generate higher level features using term frequency and co-occurrence information of highly indicative features in huge amount of unlabeled data. We examine its performance in a named entity classification task, which is designed to remove non-gene entries in a large dictionary derived from online resources. The results show that new features generated by FCG outperform lexical features by 5.97 F-score and 10.85 for OOV terms. Also in this framework each extension yields significant improvements and the sparse lexical features can be transformed into both a lower dimensional and more informative representation. A forward maximum match method based on the refined dictionary produces an F-score of 86.2 on BioCreative 2 GM test set. Then we combined the dictionary with a conditional random field (CRF) based gene mention tagger, achieving an F-score of 89.05, which improves the performance of the CRF-based tagger by 4.46 with little impact on the efficiency of the recognition system. A demo of the NER system is available at <url>http://202.118.75.18:8080/bioner</url>.</p

Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. Previous attempts to incorporate hierarchical structure have yielded little benefit and are restricted to shallow ontologies. This paper presents new methods using real and complex bilinear mappings for integrating hierarchical information, yielding substantial improvement over flat predictions in entity linking and fine-grained entity typing, and achieving new state-of-the-art results for end-to-end models on the benchmark FIGER dataset. We also present two new human-annotated datasets containing wide and deep hierarchies which we will release to the community to encourage further research in this direction: MedMentions, a collection of PubMed abstracts in which 246k mentions have been mapped to the massive UMLS ontology; and TypeNet, which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k entity types. In experiments on all three datasets we show substantial gains from hierarchy-aware training.Comment: ACL 201

Exploring the boundaries: gene and protein identification in biomedical text

Background: Good automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools. Methods: We present a maximum-entropy based system incorporating a diverse set of features for identifying gene and protein names in biomedical abstracts. Results: This system was entered in the BioCreative comparative evaluation and achieved a precision of 0.83 and recall of 0.84 in the “open ” evaluation and a precision of 0.78 and recall of 0.85 in the “closed ” evaluation. Conclusions: Central contributions are rich use of features derived from the training data at multiple levels of granularity, a focus on correctly identifying entity boundaries, and the innovative use of several external knowledge sources including full MEDLINE abstracts and web searches. Background The explosion of information in the biomedical domain and particularly in genetics has highlighted the need for automated text information extraction techniques. MEDLINE, the primary research database serving the biomedical community, currently contains over 14 million abstracts, with 60,000 new abstracts appearing each month. There is also an impressive number of molecular biological databases covering a

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

Extraction of semantic biomedical relations from text using conditional random fields

<p>Abstract</p> <p>Background</p> <p>The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition.</p> <p>Results</p> <p>We benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph.</p> <p>Conclusion</p> <p>We extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining. Current work is focused on improving the accuracy of detection of entities as well as entity boundaries, which will also greatly improve the relation extraction performance.</p