Lecture

Information retrieval: vector space

Related lectures (32)

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

Information Retrieval Basics

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

Information Retrieval Basics: Boolean and Vector Space Models

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

Probabilistic Retrieval

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

Information Retrieval: Fagin's Algorithm

Covers the implementation of Fagin's algorithm for information retrieval, focusing on efficient document retrieval.

Latent Semantic Indexing: Concepts and Applications

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

Vector Space Semantics (and Information Retrieval)

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

Text Data Processing: Basics & Techniques

Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.

Handling Text: Document Retrieval, Classification, Sentiment Analysis

Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.

Latent Semantic Indexing: Concepts and Applications

Explores latent semantic indexing, vocabulary construction, document matrix creation, query transformation, and document retrieval using cosine similarity.

Vector Space Retrieval Exercise

Covers TF-IDF computation, document vectors, cosine similarity, and precision formulas.

Information Retrieval Basics: Overview and Text-Based Retrieval

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

Document Retrieval and Classification

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

Text Retrieval: Document Ranking

Covers text retrieval tasks with document ranking and re-ranking, using a large corpus for evaluation.

Text Data Analysis: Techniques and Applications

Explores handling text data, deriving clean datasets from unstructured text, and various text analysis techniques.

Text-Based Information Retrieval

Covers the basic concepts of text-based information retrieval and how documents are indexed and retrieved based on user queries.

Indexing and Distributed Retrieval

Explores indexing techniques, inverted files, map-reduce algorithms, and top-k document retrieval methods in text retrieval systems.

Embedding Models: Concepts and Retrieval

Covers embedding models for document retrieval, latent semantic indexing, SVD, and topic models.

Latent Semantic Indexing

Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.

Information Retrieval Basics: Document Length and Normalization

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