期刊名称：International Journal of Computer Science and Communication Networks
电子版ISSN：2249-5789
出版年度：2012
卷号：2
期号：4
页码：491-495
出版社：Technopark Publications
摘要：Pipelining is a process in which a complex task is divided into many stages that are solved sequentially. A pipeline is composed of a number of elements (processes, threads, co routines, etc.), arranged in such a way so that the output of each element is fed as input to the next in the sequence. Many machine learning problems are also solved using a pipeline model. Pipelining plays a very important role in applying the machine learning solutions efficiently to various natural language processing problems. The use of pipelining results in the better performance of these systems. However, these systems usually result in considerable computational complexity. For this reason researchers were motivated for using active learning for these systems. Reason of using active learning is that these algorithms perform better than the traditional learning algorithms keeping the training data same. In this paper we discuss an active learning strategy for pipelining of an important natural language processing task i.e. information extraction.
