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Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved goal of NLP. A key factor impeding its solution by machine learned systems is the limited availability of human-annotated data. Hermann et al. (2015) seek to solve this problem by creating over a million training examples by pairing CNN and(More)
A central challenge in relation extraction is the lack of supervised training data. Pattern-based relation extractors suffer from low recall, whereas distant supervision yields noisy data which hurts precision. We propose bootstrapped self-training to capture the benefits of both systems: the precision of patterns and the generalizability of trained models.(More)
This paper introduces the use of magnetic field tomography (MFT), a noninvasive technique based on distributed source analysis of magnetoencephalography data, which makes possible the three-dimensional reconstruction of dynamic brain activity in humans. MFT has a temporal resolution better than 1 msec and a spatial accuracy of 2-5 mm at the cortical level,(More)
A method of analysing biomagnetic signals is presented which focuses attention on activity in a specific region of interest. The method is based on the construction of virtual sensors, corresponding to linear combinations of lead fields that are optimally localized within the region of interest. This method is fast and stable. It is tested against(More)
Brettanomyces spp. can present unique cell morphologies comprised of excessive pseudohyphae and budding, leading to difficulties in enumerating cells. The current cell counting methods include manual counting of methylene blue-stained yeasts or measuring optical densities using a spectrophotometer. However, manual counting can be time-consuming and has high(More)
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