Concept

Word-sense disambiguation

Related lectures (31)

Latent Semantic Indexing

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

Word Embeddings: Modeling Word Context and Similarity

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

Binary Sentiment Classifier Training

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

Document Retrieval and Classification

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

Knowledge Inference: Entity Disambiguation and Graph Embeddings

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

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.

Neural Word Embeddings

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

Optimization in Machine Learning

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

Introduction to Programming Sense

Discusses the relationship between events and actions in programming.

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.

Digital Urban History: Lausanne Time Machine

Explores the complexities of digitizing historical urban records and emphasizes the importance of linking information for comprehensive analysis.

Entity Disambiguation and Link Prediction

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

Learning Policies from Non-Traditional Sources

Explores learning interactive policies from non-traditional sources of data for autonomous systems, including language-informed latent actions and the PLATO framework.

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.

Inhabitable Habitat: Understanding Urban Hospitality

Delves into the challenges faced by newcomers in finding suitable living conditions in urban environments.

Modern NLP: A Simple Neural Classifier

Introduces the basics of modern NLP, covering word embeddings, neural models, and tasks like sequence labeling and text generation.

Neural Networks for NLP

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Lexical Semantics

Explores lexical semantics, word sense, semantic relations, and WordNet, highlighting applications in language engineering and information retrieval.

Text Handling: Matrix, Documents, Topics

Explores text handling, focusing on matrices, documents, and topics, including challenges in document classification and advanced models like BERT.

Entity Disambiguation

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