A Conceptual Framework of Online Natural Language Processing Pipeline Application

This paper describes a conceptual framework that enables online NLP pipelined applications to solve various interoperability issues and data exchange problems between tools and platforms; e.g., tokenizers and part-of-speech taggers from GATE, UIMA, or other platforms. We propose a restful wrapping solution, which allows for universal resource identification for data manage-ment, a unified interface for data exchange, and a light-weight serialization for data visualization. In addition, we propose a semantic mapping-based pipeline composition, which allows experts to interactively exchange data between heterogeneous components.