We address the problem of weakly-supervised relation extraction in hospital discharge summaries. Sentences with pre-identified concept types (for example: medication, test, problem, symptom) are labeled with the relationship between the concepts. We present a novel technique for weakly-supervised bootstrapping of a classifier for this task: Groundtruth Budgeting. In the case of highly-overlappi...

Seed sampling is critical in semi-supervised learning. This paper proposes a clusteringbased stratified seed sampling approach to semi-supervised learning. First, various clustering algorithms are explored to partition the unlabeled instances into different strata with each stratum represented by a center. Then, diversity-motivated intra-stratum sampling is adopted to choose the center and addi...

Susan Carey's account of Quinean bootstrapping has been heavily criticized. While it purports to explain how important new concepts are learned, many commentators complain that it is unclear just what bootstrapping is supposed to be or how it is supposed to work. Others allege that bootstrapping falls prey to the circularity challenge: it cannot explain how new concepts are learned without pres...

Bootstrapping has recently become the focus of much attention in natural language processing to reduce labeling cost. In bootstrapping, unlabeled instances can be harvested from the initial labeled “seed” set. The selected seed set affects accuracy, but how to select a good seed set is not yet clear. Thus, an “iterative seeding” framework is proposed for bootstrapping to reduce its labeling cos...

Distributed Hash Table (DHT)-based protocols are new approaches proposed to Wireless Sensor Networks (WSN). Their main advantage resides on the easy integration of DHT-based WSN into the Internet Of Things. However, these protocols face multiple challenges in their bootstrapping phase, especially at the case of randomly deployed WSN. We presented in a recent work a bootstrapping protocol for us...

We introduce a general technique for making statistical inference from clustering tools applied to gene expression microarray data. The approach utilizes an analysis of variance model to achieve normalization and estimate differential expression of genes across multiple conditions. Statistical inference is based on the application of a randomization technique, bootstrapping. Bootstrapping has p...

We present a semi-supervised (bootstrapping) approach to the extraction of time expression mentions in large unlabelled corpora. Because the only supervision is in the form of seed examples, it becomes necessary to resort to heuristics to rank and filter out spurious patterns and candidate time expressions. The application of bootstrapping to time expression recognition is, to the best of our k...

Despite the undisputed prominence of inheritance as the fundamental reuse mechanism in object-oriented programming languages, the variants – single inheritance, multiple inheritance, and mixin inheritance – all suffer from conceptual and practical problems. Traits overcome the problems arising with the different variants of inheritance. Traits are essentially groups of methods that serve as bui...

SVBRDF bootstrapping is a new method for data-driven modeling of real-world, spatially-varying reflectance that provides a high resolution result in both the spatial and angular domains. It decomposes reflectance measurement into two phases. The first acquires representatives of high angular dimension but sampled sparsely over the surface, while the second acquires keys of low angular dimension...