The explosive growth of data produced by different devices and applications has contributed to the abundance of big data. To process such amounts of data efficiently, strategies such as De-duplication has been employed. Among the three different levels of de-duplication named as file level, block level and chunk level, De-duplication at chunk level also known as byte level is the most popular a...

Given the size of today’s data, out-of-core visualization techniques are increasingly important in many domains of scientific research. In earlier work a technique called dynamic chunking [1] was proposed that can provide significant performance improvements for an out-of-core, arbitrary direction slicer application. In this work we validate dynamic chunking for several common data access patte...

Grouping words into meaningful chunks is a fundamental process for fluent reading. The present study is an attempt to understand the relationship between chunking and second language (L2) reading fluency. The effects of text segmentation on comprehension, rate, and regression in L2 reading were investigated using a self-paced reading task in a moving-window condition. The participants were inte...

We present a memory based learning MBL approach to shallow parsing in which POS tagging chunking and identi cation of syntactic relations are formulated as memory based modules The experiments reported in this paper show competitive results the F for the Wall Street Journal WSJ treebank is for NP chunking for VP chunking for subject detection and for object detection

This paper presents a chunking-based discriminative approach to full parsing. We convert the task of full parsing into a series of chunking tasks and apply a conditional random field (CRF) model to each level of chunking. The probability of an entire parse tree is computed as the product of the probabilities of individual chunking results. The parsing is performed in a bottom-up manner and the ...