Learning for Text and Graph Data (Labs)

The course aims at providing an introduction to advanced machine learning and combinatorial methods for large-scale text and graph data. The syllabus included:

• Advanced graph kernels and classification, clustering / community mining (Louvain, modularity, degeneracy)
• Influence maximization models (SIR/SIS, LT, IC, …), degeneracy-based spreader selection
• Graph-of-words advanced topics: TW-ICW, graph kernels for document similarity, graph-based regularization for text classification
• Word embeddings, unsupervised document classification with the Word Mover’s Distance, WMD vs. cosine similarity
• Deep learning for NLP, supervised document classification (TF-IDF vs. TW-IDF)
• Keyword extraction for summarization: graph-based keyword extraction, summarization (offline, online), Filippova’s word graph for multi-sentence fusion