These days, global pool of data is growing at 2.5 quintillion byte per day and more than 90 percent of this huge pool of data has been produced in the last two years alone [1]. The era of big data has arrived. After [2] explained the file system of Google in this way such that files are split in to various chunks stored in a redundant fashion on a cluster or commodity machines, most of research...

Predictive shift-reduce (PSR) parsing for a subclass of hyperedge replacement graph grammars has recently been devised by Frank Drewes and the authors [6]. This paper describes in detail how efficient PSR parsers are generated with the Grappa parser generator implemented by Mark Minas. Measurements confirm that the generated parsers run in linear time.

This paper explores FrameNet as a resource for building a lexicon for deep syntactic and semantic parsing with a practical multipledomain parser. The TRIPS parser is a wide-coverage parser which uses a domain-independent ontology to produce semantic interpretations in 5 different application domains. We show how semantic information from FrameNet can be useful for developing a domainindependent...

A parsing method called buffered shift-reduce parsing is presented, which adds an intermediate buffer (queue) to the usual LR parser. The buffer’s usage is analogous to that of the wait-and-see parsing, but it has unlimited buffer length, and may serve as a separate reduction (pruning) stack. The general structure of its parser and some features of its grammars and parsing tables are discussed.

Sentiment analysis is the process of analyzing a person’s perception or belief about a particular subject matter. However, finding correct opinion or interest from multi-facet sentiment data is a tedious task. In this paper, a method to improve the sentiment accuracy by utilizing the concept of categorized dictionary for sentiment classification and analysis is proposed.  A categorized dictiona...

The Map/Reduce framework is a programming model recently introduced by Google Inc. to support distributed computing on very large datasets across a large number of machines. It provides a simple but yet powerful way to implement distributed applications without having deeper knowledge of parallel programming. Each participating node executes Map and/or Reduce tasks which involve reading and wri...

Big data is a commodity that highly valued in the entire globe. It not just regarded as but world of experts, we can derive intelligence from it. Because its characteristics which are Variety, Value, Volume, Velocity, and growing need how it be handled, Organizations facing difficulties ensuring optimal well affordable processing storage large datasets. One already existing models used for rapi...