A Survey of Text Mining Architectures and the UIMA Standard

Abstract

With the rising amount of digitally available text, the need for efficient processing algorithms is growing fast. Although a lot of libraries are commonly available, their modularity and interchangeability is very limited, therefore forcing a lot of reimplementations and modifications not only in research areas but also in real world application scenarios. In recent years, different NLP frameworks have been proposed to provide an efficient, robust and convenient architecture for information processing tasks. This paper will present an overview over the most common approaches with their advantages and shortcomings, and will discuss them with respect to the first standardized architecture - the Unstructured Information Management Architecture (UIMA).

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