Introduction to Information Retrieval

Related lectures (32)

Explores optimization techniques, word embeddings, and recommendation systems in machine learning.

Covers word embeddings, modeling word context and similarity in a low-dimensional space.

Explores word embeddings, context importance, and learning algorithms for creating new representations.

Explores word embeddings, topic models, Word2vec, Bayesian Networks, and inference methods like Gibbs sampling.

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

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

Explores text retrieval systems, inverted files, addressing granularity, and access structures in information retrieval.

Delves into the processing of large digital text collections, exploring hidden regularities, text reuse, and TF-IDF analysis.

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

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

Covers the training of a binary sentiment classifier using an RNN.

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