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incorporate aspects of many other disciplines (such as artificial intelligence, computer science, and lexicography). Yet it continues to be the Holy Grail of those who try to make computers deal intelligently with one of the most complex characteristics of human beings: language.

Language is so fundamental to humans, and so ubiquituous, that fluent use of it is often considered almost synonymous with intelligence. Given that, it is not surprising that computers have difficulty with natural language. Nonetheless, many people seem to think it should be easy for computers to deal with human language, just because they themselves do so easily.

Research in both speech recognition (i.e., literal transcription of spoken words) and language processing (i.e., understanding the meaning of a sequence of words) has been going on for decades. But quite recently, speech recognition started to make the transition from laboratory to widespread successful use in a large number of different kinds of systems. What is responsible for this technology transition?

Two key features that have allowed the development of successful speech recognition systems are (1) a simple general description of the speech recognition problem (which results in a simple general way to measure the performance of recognizers) and (2) a simple general way to automatically train a recognizer on a new vocabulary or corpus. Together, these features helped to open the floodgates to the successful, widespread application of speech recognition technology. Many of the papers in this volume, particularly those by Makhoul and Schwartz, Jelinek, Levinson, Oberteuffer, Weinstein, and Wilpon attest to this fact.

But it is important to distinguish ''language understanding" from "recognizing speech," so it is natural to ask, why the same path has not been followed in natural language understanding. In natural language processing (NLP), as we shall see, there is no easy way to define the problem being solved (which results in difficulty evaluating the performance of NL systems), and there is currently no general way for NL systems to automatically learn the information they need to deal effectively with new words, new meanings, new grammatical structures, and new domains.

Some aspects of language understanding seem tantalizingly similar to problems that have been solved (or at least attacked) in speech recognition, but other aspects seem to emphasize differences that may never allow the same solutions to be used for both problems. This paper briefly touches on some of the history of NLP, the types of NLP and their applications, current problem areas and suggested solutions, and areas for future work.

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