Probabilistic Retrieval

Related lectures (32)

Page 1 of 4

Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.

Covers probabilistic information retrieval, query likelihood models, language modeling, and relevance feedback algorithms.

Covers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.

Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.

Explores practical relevance feedback in probabilistic retrieval and query optimization.

Covers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.

Explores the Vector Space model, Bag of Words, tf-idf, cosine similarity, Okapi BM25, and Precision and Recall in Information Retrieval.

Explores Latent Semantic Indexing, a technique for mapping documents into a concept space for retrieval and classification.

Covers Probabilistic Information Retrieval, including Query Likelihood Model, Language Modeling, and smoothing techniques for non-occurring terms.

Covers the basics of information retrieval, including text-based retrieval and indexing techniques.