Lectures related to Lexical Semantics

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.

Handling Text: Document Retrieval, Classification, Sentiment Analysis

Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.

Word Embeddings: Models and Applications

Explores word embeddings, models like CBOW and Skipgram, Fasttext, Glove, subword embeddings, and their applications in document search and classification.

Neural Word Embeddings: Learning Representations for Natural Language

Covers neural word embeddings and methods for learning word representations in natural language processing.

Information Retrieval Basics

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

Binary Sentiment Classifier Training

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

Neural Word Embeddings

Introduces neural word embeddings and dense vector representations for natural language processing.

Word Embeddings: Introduction and Applications

Introduces word embeddings, explaining how they capture word meanings based on context and their applications in natural language processing tasks.

Entity Disambiguation and Link Prediction

Explores entity disambiguation, linking text to knowledge bases, and link prediction in knowledge graphs with examples from Wikipedia.

Understanding Knowledge Graphs MOOC: Simulation Neurocience

Explores the concept of Knowledge Graphs and their role in data integration and semantic understanding, showcasing real-world examples and applications.

Latent Semantic Indexing Log in to Mediaspace to watch this video

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

Information Retrieval Indexing: Latent Semantic Indexing Log in to Mediaspace to watch this video

Explores Latent Semantic Indexing in Information Retrieval, discussing algorithms, challenges in Vector Space Retrieval, and concept-focused retrieval methods.

Knowledge Representation: Semantics and Data Structures Log in to Mediaspace to watch this video

Explores knowledge representation, data structures, semantics, and the challenges of searching for data on the web.

Introduction to NLP and the Course Log in to Mediaspace to watch this video

Covers the basics of Natural Language Processing, including challenges, linguistic processing levels, and the impact of power laws.

Latent Semantic Indexing Log in to Mediaspace to watch this video

Covers Latent Semantic Indexing, a method to improve information retrieval by mapping documents and queries into a lower-dimensional concept space.

Semantic Web & Information Extraction Log in to Mediaspace to watch this video

Explores Semantic Web, ontologies, information extraction, key phrases, named entities, and knowledge bases.

Introduction to Information Retrieval Log in to Mediaspace to watch this video

Introduces the basics of information retrieval, covering text-based retrieval, document features, similarity functions, and the difference between Boolean and ranked retrieval.

Information Retrieval Basics Log in to Mediaspace to watch this video

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

Word Embeddings: Glove and Semantic Relationships Log in to Mediaspace to watch this video

Explores word embeddings, Glove model, semantic relationships, subword embeddings, and syntactic relationships.

Word Embeddings: Modeling Word Context and Similarity Log in to Mediaspace to watch this video

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