Understand the basic concepts and terminology in natural language study

Access and use  natural language corpora

Recognize the statistical properties of text from raw corpora

Create finite state automata and finite state transducers for morphological analysis of words

Design two level compilers for morphological analysis

Understand sequence classification methods

Apply the supervised training algorithm for HMMs for POS tagging

Calculate probabillities of POS tag sequences

Apply dynamic programming algorithms such as Viterbi to choose the best POS tag sequence for a given sentence

Design part-of-speech taggers from scratch

Recognize the limitations of context free grammars and analyse the extensions to CFG

Apply well-known parsing algorithms to natural language data

Recognize the limitations of parsing algorithms and improve them using differnet methods.

Understand the principles of natural language meaning

Analyse and Compare different representations for semantics

Describe state of the art methods for NLP applications such as text categorization, information extraction etc.

Recognize the computational problems associated to natural language recognition and parsing

Design , implement and evaluate an NLP system