Lecture
Word Embeddings: Models and Applications
Related lectures (32)
Word Embedding Models: Optimization and Applications
Explores optimizing word embedding models, including loss function minimization and gradient descent, and introduces techniques like Fasttext and Byte Pair Encoding.
Word Embeddings: Glove and Semantic Relationships
Explores word embeddings, Glove model, semantic relationships, subword embeddings, and syntactic relationships.
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.
Latent semantic indexing: inverted files
Explores term-offset indices in inverted files and relevance feedback solutions.
Word Embeddings: Modeling Word Context and Similarity
Covers word embeddings, modeling word context and similarity in a low-dimensional space.
Word Embeddings: Models and Learning
Explores word embeddings, context importance, and learning algorithms for creating new representations.
Latent Semantic Indexing: Concepts and Applications
Explores Latent Semantic Indexing, a technique for mapping documents into a concept space for retrieval and classification.
Information Retrieval Indexing: Latent Semantic Indexing
Explores Latent Semantic Indexing in Information Retrieval, discussing algorithms, challenges in Vector Space Retrieval, and concept-focused retrieval methods.
Link-based Ranking: Fundamentals and Algorithms
Covers the fundamentals and algorithms of link-based ranking, including anchor text indexing, PageRank, HITS, and practical implementations.
Information Retrieval Basics
Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.