INTEGRATED DEVELOPMENT ENVIRONMENTS FOR NATURAL LANGUAGE PROCESSING

INTEGRATED DEVELOPMENT ENVIRONMENTS FOR NATURAL LANGUAGE PROCESSING

13 Pages · 2001 · 308 KB · English

INTEGRATED DEVELOPMENT ENVIRONMENTS The purpose of an IDE is to accelerate the development of an accurate, fast, robust, and maintainable application.

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Copyright © 2001 by Text Analysis International, Inc. All rights reserved. INTEGRATED DEVELOPMENT ENVIRONMENTS FOR NATURAL LANGUAGE PROCESSING Text Analysis International, Inc. 1669-2 Hollenbeck Ave. #501 Sunnyvale, California 94087 Copyright © 2001 by Text Analysis International, Inc. All rights reserved. October 2001 Copyright © 2001 by Text Analysis International, Inc. All rights reserved. INTRODUCTION From the time of the first Russian-to-English translation projects of the 1950s, a tremendous amount of research and development has been expended to automate the analysis and understanding of unstructured or "free" text. Children universally become fluent in their native language within the first few years of life, leading us to expect that language should be easy to process using computers. However, language processing has proved to be an extremely complex field of endeavor. Natural Language Processing (NLP) is the branch of Artificial Intelligence (AI) devoted to this area, along with its sister discipline, Computational Linguistics (CL). Knowledge Representation (KR) theories also play an important role in the use of linguistic, conceptual, and domain knowledge required for properly automating language processing. The past ten years have seen rapid and substantial growth in the commercialization of NLP, along with the widespread assessment that NLP is currently a critical technology for business. Two trends are largely responsible for this: the ascent of Natural Language Engineering (NLE) and statistical NLP. Both arose in response to the brittleness of previous approaches based on formal theories and crudely handcrafted systems. NLE emerged almost overnight, propelled by successes such as the FASTUS system [REF SRI, Hobbs et al, 1993] at the fourth DARPA Message Understanding Conference (MUC-4). Here, an internationally respected research group that had spent decades on a formal approach to language processing reversed course and implemented an "unprincipled" information extraction (IE) system that achieved among the highest scores in competitive blind testing. Statistical NLP approaches have yielded strong commercial successes in recent years, primarily in text categorization applications. The popularity of weak methods such as Bayesian statistics, machine learning, and neural networks

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