Section I: Machine Learning and Kernel-based Learning.
Supervised methods. Probabilistic and Generative Methods. Unsupervised Learning. Clustering. Semantic Similarity metrics Agglomerative clustering methods. K-mean.
Markov Models. Hidden Markov Models.
Kernel-based Learning. Polynomial and RBF Kernels. String Kernels. Tree kernels. Latent Semantic kernels. Semantic kernels. Applications
Section II: Statistical Language Processing
Supervised Language Processing tools. HMM-based POS tagging. Named Entity Recognition. Statistical parsing. PCFGs: Charniak parser. Lexicalized Parsing Methods. Shallow Semantic Parsing: kernel based semantic role labelling. Information Extraction.
Section III: Web Mining & Retrieval.
Ranking Models for the Web. Introduction to Social Network Analysis: rank, centrality.
Random walk models: Page Rank. Web Search Engines. SEO. Google.
Preference Learning for IR.
Question Answering Systems. Open-domain Information Extraction.
Wikipedia knowledge Acquisition. Social Web. Graph-based algorithms for community detection.
Introduction to Opinion Mining and Sentiment Analysis.
The course aims at:
• Introducing and exploring topics related to data-driven algorithms for the induction of knowledge from large scale data collections;
• Presenting the major data models underlying Web search engines and for Enterprise Search
• Studying technologies and formalisms for the treatment of unstructured Web data through Artifical Intelligence and Natural Language Processing methods and for the linguistic processing of texts and Social Web data
• Introducing experimental practices in application such as Semantic document management, Web Network Analysis and Opinion Mining.