Sillabo dell'insegnamento

• B - Algoritmi
TAA - Tecniche Algoritmiche Avanzate
Boolean retrieval, Search Engines, Relevance Feedback & Query Expansion, Document Filtering and Categorization, flat and hierarchical clustering, Latent Semantic Analysis, Web Crawling and the Google algorithm.
* A - Algoritmi fondamentali
Fine-Grained Opinion Mining: automatic review classification, deep opinion analysis, automatic product extraction and review, reputation/social media analysis.
V - Varie
Deep Linguistic Analysis for Question Answering: QA tasks (open, restricted, factoid, non-factoid), NLP Representation, Question Answering Workflow, QA Pipeline, Question Classification and QA reranking.
• M - Rappresentazione della conoscenza
BC - Basi di Conoscenza
Vector space model, Feature Vectors
* SBC - Sistemi Basati su Conoscenza
Document Filtering and Categorization, flat and hierarchical clustering, Latent Semantic Analysis
AASC - Apprendimento Automatico e Scoperta di Conoscenza
Statistical Machine Learning: Kernel Methods, Classification, Clustering, Ranking, Re-Ranking and Regression and hints to practical machine learning.