NLP is a large and multidisciplinary field, so this course can only provide a very general introduction. The first chapter is designed to give an overview of the main subareas and a very brief idea of the main applications and the methodologies which have been employed. The history of NLP is briefly discussed as a way of putting this into perspective. The next three chapters describe some of the main subareas in more detail. The organisation is roughly based on increased `depth' of processing, starting with relatively surface-oriented techniques and progressing to considering meaning of sentences and meaning of utterances in context. Each chapter will consider the subarea as a whole and then go on to describe one or more sample algorithms which tackle particular problems. The algorithms have been chosen because they are relatively straightforward to describe and because they illustrate a specific technique which has been shown to be useful, but the idea is to exemplify an approach, not to give a detailed survey (which would be impossible in the time available). However, other approaches will sometimes be discussed briefly. The final chapter brings the preceding material together in order to describe the state of the art in sample applications.
The objective of my book for the students is to:
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be able to describe the architecture of and basic design for a generic NLP system `shell'.
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be able to discuss the current and likely future performance of several NLP applications, such as machine translation and email response.
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be able to briefly describe a fundamental technique for processing language for several subtasks, such as morphological analysis, syntactic parsing, word sense disambiguation etc.
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understand how these techniques draw on and relate to other areas of (theoretical) computer science, such as formal language theory, formal semantics of programming languages, or theorem proving.
