Annotation-Based Finite State Processing in a Large-Scale NLP Architecture

There are well-articulated arguments promoting the deployment of finite-state (FS) processing techninques for natural language processing (NLP) application development. This paper adopts a point of view of designing industrial strength NLP frameworks, where emerging notions include a pipelined architecture, open-ended intercomponent communication, and the adoption of linguistic annotations as fundamental analytic/descriptive device. For such frameworks, certain issues arise—operational and notational—concerning the underlying data stream over which the FS machinery operates. The paper reviews recent work on finite-state processing of annotations and highlight some essential features required from a congenial architecture for NLP aiming to be broadly applicable to, and configurable for, an open-ended set of tasks.

By: Branimir K. Boguraev

Published in: RC23393 in 2004


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