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BACKGROUND AND OBJECTIVE In order for computers to extract useful information from unstructured text, a concept normalization system is needed to link relevant concepts in a text to sources that contain further information about the concept. Popular concept normalization tools in the biomedical field are dictionary-based. In this study we investigate the(More)
We present a multi-stream bi-directional recurrent neu-ral network for fine-grained action detection. Recently, two-stream convolutional neural networks (CNNs) trained on stacked optical flow and image frames have been successful for action recognition in videos. Our system uses a tracking algorithm to locate a bounding box around the person, which provides(More)
VRFP is a real-time video retrieval framework based on short text input queries in which weakly labeled training samples from the web are obtained, after the query is known. Our experiments show that a Fisher Vector is robust to noise present in web-images and compares favorably in terms of accuracy to other standard representations. While a Fisher Vector(More)
Recognition of medical concepts is a basic step in information extraction from clinical records. We wished to improve on the performance of a variety of concept recognition systems by combining their individual results. We selected two dictionary-based systems and five statistical-based systems that were trained to annotate medical problems, tests, and(More)
Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be(More)
Many biomedical relation extraction systems are machine-learning based and have to be trained on large annotated corpora that are expensive and cumbersome to construct. We developed a knowledge-based relation extraction system that requires minimal training data, and applied the system for the extraction of adverse drug events from biomedical text. The(More)
Complex event retrieval is a challenging research problem , especially when no training videos are available. An alternative to collecting training videos is to train a large semantic concept bank a priori. Given a text description of an event, event retrieval is performed by selecting concepts linguistically related to the event description and fusing the(More)
PURPOSE Pharmacovigilance methods have advanced greatly during the last decades, making post-market drug assessment an essential drug evaluation component. These methods mainly rely on the use of spontaneous reporting systems and health information databases to collect expertise from huge amounts of real-world reports. The EU-ADR Web Platform was built to(More)
With the growing importance of large network models and enormous training datasets, GPUs have become increasingly necessary to train neu-ral networks. This is largely because conventional optimization algorithms rely on stochastic gradient methods that don't scale well to large numbers of cores in a cluster setting. Furthermore , the convergence of all(More)