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Lecture Synopsis
Aims
This course aims to introduce the fundamental techniques of natural language processing, to develop an understanding of the limits of those techniques and of current research issues, and evaluate some current and potential applications.
• Introduction. Brief history of NLP research, current applications, generic NLP system architecture, knowledgebased versus probabilistic approaches.
• Finite state techniques. Inflectional and derivational morphology, finite-state automata in NLP, finite-state transducers.
• Prediction and part-of-speech tagging. Corpora, simple N-grams, word prediction, stochastic tagging, evaluating system performance.
• Parsing and generation I. Generative grammar, context-free grammars, parsing and generation with contextfree grammars, weights and probabilities.
• Parsing and generation II. Constraint-based grammar, unification, simple compositional semantics.
• Lexical semantics. Semantic relations, WordNet, word senses, word sense disambiguation.
• Discourse. Anaphora resolution, discourse relations.
• Applications. Machine translation, email response, spoken dialogue systems.
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https://www.cl.cam.ac.uk/teaching/2002/NatLangProc/nlp1-4.pdf
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