Natural Language Processing 2016/2017

·        Introduction to NLP and to the challenge of talking machines.

·        The language: linguistic models and theories

·        Linguistic models and systems.

-        How to determine that a model is correct and a system is effective: inter-annotation agreement and statistical significance

-        Morpholgy: Finite state automaton and transducers

-        Syntactic analysis with context-free grammars

§  Parsing with context-free grammars

§  Feature Structures and Unification

§  Tree Adjoining Grammars

§  Modular and Lexicalized Parsing

§  Probabilistic context-free grammar

-        Semantics

§  Symbolic Semantic Representation: WordNet and FrameNet

§  Lambda Calcolus for natural languagte semantics

§  Distributional semantics

-        Textual Entailment Recognition

TESTI ADOTTATI

• Daniel Jurafsky and James H. Martin, SPEECH and LANGUAGE PROCESSING: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (Second Edition)
• I.Dagan, D.Roth, M.Sammons, F.M.Zanzotto, Recognizing Textual Entailment: Models and Applications, Synthesis Lectures on Human Language Technologies #23, Morgan&Claypool Publishers, 2013