Concept

Relevance (information retrieval)

Related lectures (32)

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

Information Retrieval Basics: Document Length and Normalization

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

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.

Probabilistic Retrieval

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

Information Retrieval Basics

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

Information Retrieval Basics

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

Probabilistic Retrieval Models

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

Vector Space Retrieval Exercise

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

Question Answering: Challenges and Solutions

Explores challenges in question answering systems and solutions, including fine-tuning and open-domain retrieval.

Information Retrieval Basics: Overview and Text-Based Retrieval

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

Enhancements in Search Functionality

Introduces a new search feature allowing users to search within people's profiles.

Information Retrieval: Fagin's Algorithm

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

Handling Text: Document Retrieval & Classification

Explores document retrieval, classification, sentiment analysis, and topic detection in text analysis using supervised learning and bag-of-words models.

Query Expansion: Methods and Algorithms

Explores query expansion methods, user relevance feedback, Rocchio algorithm, and practical considerations in expanding queries.

Latent Semantic Indexing

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

Information Retrieval Basics: Document Frequency and Precision

Introduces information retrieval basics, emphasizing document frequency and precision in evaluating retrieval quality.

Information Retrieval: Basics and Techniques

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

Text Retrieval: Document Ranking

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

Untitled

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.