Vector Space Retrieval Exercise

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

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

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

Explores document length, normalization, bias compensation, and retrieval model evaluation in information retrieval.

Introduces the basics of Information Retrieval, covering indexing, weighting schemes, cosine similarity, and query evaluation.

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

Introduces Boolean and Vector Space models for information retrieval, covering syntax, similarity computation, term frequency, and query weights.

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors like BERT.

Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.