Information Retrieval: Basics and Techniques

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Related lectures (32)

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Latent Semantic Indexing: Concepts and Applications

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

Text Data Processing: Basics & Techniques

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

Text Data Analysis: Techniques and Applications

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

Latent semantic indexing: inverted files

Explores term-offset indices in inverted files and relevance feedback solutions.

Text Data Analysis: Basics and Techniques

Introduces the basics of text data analysis, covering document retrieval, classification, sentiment analysis, and topic detection using preprocessing techniques and machine learning models.

Latent Semantic Indexing

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

Document Retrieval and Classification

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

Information Retrieval: Fagin's Algorithm

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

Information Retrieval Basics: Overview and Text-Based Retrieval

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

Indexing and Distributed Retrieval

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