We classify and review current approaches to software infrastructure for research, development and delivery of NLP systems. The task is motivated by a discussion of current trends in the field of NLP and Language Engineering. We describe a system called GATE (a General Architecture for Text Engineering) that provides a software infrastructure on top of which heterogeneous NLP processing modules may be evaluated and refined individually, or may be combined into larger application systems. GATE aims to support both researchers and developers working on component technologies (e.g. parsing, tagging, morphological analysis) and those working on developing end-user applications (e.g. information extraction, text summarisation, document generation, machine translation, and second language learning). GATE promotes reuse of component technology, permits specialisation and collaboration in large-scale projects, and allows for the comparison and evaluation of alternative technologies. The first release of GATE is now available. the original text is preserved in processing only as parts of an integrated data structure that represents information about the text in a uniform theoretically-motivated model, e.g. attribute-value structures in the ALEP system (Simkins, 1994) .
In REF , GATE is described as "a software infrastructure on top of which heterogeneous NLP processing modules may be evaluated and refined individually or may be combined into larger application systems." Thus, both Talent and GATE address the needs of researchers and developers, on the one hand, and of application builders, on the other.
Software Infrastructure For Natural Language Processing
Applied Natural Language Processing Conference
Computer Science