This paper introduces GATE, a framework and graphical development environment designed to facilitate the robust development and deployment of language engineering components and resources. GATE's architecture separates low-level tasks from data structures and algorithms, automates performance measurement, reduces integration overheads, and provides a baseline set of language processing components that can be extended or replaced. The framework supports multiple languages through its Unicode support and offers a unified data structure for smooth communication between components. GATE includes reusable modules for basic language processing tasks, such as POS tagging and semantic tagging, and supports multilingual data processing using Unicode. The paper also describes several applications developed using GATE, including MUSE, ACE, and MUMIS, and discusses the evaluation mechanisms provided by GATE, such as the AnnotationDiff tool and the benchmarking tool. Finally, the paper reviews related work and outlines future directions, including the integration of learning resources and language generation modules.This paper introduces GATE, a framework and graphical development environment designed to facilitate the robust development and deployment of language engineering components and resources. GATE's architecture separates low-level tasks from data structures and algorithms, automates performance measurement, reduces integration overheads, and provides a baseline set of language processing components that can be extended or replaced. The framework supports multiple languages through its Unicode support and offers a unified data structure for smooth communication between components. GATE includes reusable modules for basic language processing tasks, such as POS tagging and semantic tagging, and supports multilingual data processing using Unicode. The paper also describes several applications developed using GATE, including MUSE, ACE, and MUMIS, and discusses the evaluation mechanisms provided by GATE, such as the AnnotationDiff tool and the benchmarking tool. Finally, the paper reviews related work and outlines future directions, including the integration of learning resources and language generation modules.