Information Retrieval Basics

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Related lectures (32)

Page 2 of 4

Embedding Models: Concepts and Retrieval

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

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.

Introduction to Information Retrieval

Introduces the basics of information retrieval, covering text-based retrieval, document features, similarity functions, and the difference between Boolean and ranked retrieval.

Text Data Analysis: Techniques and Applications

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

Text Data Processing: Basics & Techniques

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

Handling Text Data: Document Retrieval and Classification

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

Indexing and Distributed Retrieval

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

Vector Space Retrieval Exercise

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

Text Retrieval: Document Ranking

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

Latent Semantic Indexing

Covers Latent Semantic Indexing, a method to improve information retrieval by mapping documents and queries into a lower-dimensional concept space.