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Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.
Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.
Covers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.
Introduces the basics of text data analysis, covering document retrieval, classification, sentiment analysis, and topic detection using preprocessing techniques and machine learning models.
Introduces Natural Language Processing, covering text preprocessing, sentiment analysis, and topic analysis, with a focus on building a climate change risk index.
Covers Latent Semantic Indexing, a method to improve information retrieval by mapping documents and queries into a lower-dimensional concept space.
Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.
Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.
