Prerequisite courses: none, but Regular Languages and Finite Automata, Probability, Logic and Proof, and Artificial Intelligence cover relevant material
This course aims to introduce the fundamental techniques of natural
language processing and to develop an understanding of the limits of
those techniques. It aims to introduce some current research issues,
and to evaluate some current and potential applications.
Brief history of NLP research, current applications,
generic NLP system architecture,
knowledge-based versus probabilistic
and derivational morphology, finite-state automata in NLP, finite-state
Prediction and part-of-speech tagging.
Corpora, simple N-grams, word prediction, stochastic tagging,
evaluating system performance.
Parsing and generation. Generative grammar, context-free
grammars, parsing and generation with context-free grammars, weights
Parsing with constraint-based grammars. Constraint-based grammar,
Compositional and lexical semantics.
Simple compositional semantics in constraint-based grammar.
Semantic relations, WordNet, word senses,
word sense disambiguation.
Discourse and dialogue. Anaphora
resolution, discourse relations.