Practical natural language processing

<p>This course gives a practical introduction to different problems encountered within natural language processing, and some solutions.</p> <p>Students will gain practical experience in programming while solving these problems. The course is divided into four main topics: one covering basic concepts and three covering subfields of NLP – words, syntax or morphology and semantics/pragmatics.</p> <p>1. Basic concepts:</p> <ul> <li>Basic concepts in NLP.</li> <li>Automata theory and mathematical linguistics.</li> <li>Probability theory and machine learning.</li> <li>Evaluation measurement, correctness, precision, and recall.</li> </ul> <p>2. Words:</p> <ul> <li>Corpora and corpus annotation.</li> <li>Finite-state methods for segmentation and morphological analysis.</li> <li>Statistical language modeling with n-gram markov models.</li> </ul> <p>4. Syntax:</p> <ul> <li>Part-of-speech tagging and chunking/partial parsing, making use of methods within machine learning or/and finite-state technology.</li> <li>Common formal grammars, such as feature based and probabilistic context-free grammars.</li> <li>Syntactic parsing.</li> </ul> <p>5. Semantics and Pragmatics:</p> <ul> <li>Lexical semantics, lexica, Wordnet and FrameNet.</li> <li>Word sense disambiguation with machine learning.</li> <li>Text classification with machine learning.</li> </ul>