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Related lectures (32)

Page 2 of 4

Knowledge Inference: Entity Disambiguation and Graph Embeddings

Explores entity disambiguation, graph embeddings, scoring functions, and learning methods.

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.

Foundations of Information Systems: Course Overview and Key Concepts

Introduces the course on information systems, covering its structure, objectives, and foundational concepts essential for understanding data management and decision-making.

Entity & Information Extraction

Explores information extraction using classifiers, features, and syntactic analysis.

Information Retrieval Basics

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

Taxonomy Induction: Relations Extraction and Graph Construction

Covers relations extraction and graph construction in taxonomy induction, emphasizing noise reduction for accurate graphs.

Information retrieval: vector space

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

Latent Semantic Indexing: Concepts and Applications

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

Entity Disambiguation

Explores entity disambiguation techniques, including NER, Viterbi algorithm, and GPT models, emphasizing prompt design and in-context learning.

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.