Explore distributional semantics and word vectors in this comprehensive lecture from CMU's Neural Networks for NLP course. Delve into describing words by their context, counting and predicting techniques, skip-grams and CBOW models, and methods for evaluating and visualizing word vectors. Learn about advanced word vector techniques, including contextualization, WordNet, and various embedding models like CBOW and GloVe. Examine different types of context, evaluation methods for embeddings, and their practical applications in NLP systems. Discuss the limitations of embeddings and explore sub-word embedding techniques. Gain valuable insights into the foundations of word representations in natural language processing.
Overview
Syllabus
Remember: Neural Models
How to Train Embeddings?
What do we want to know about words?
Contextualization of Word Representations
A Manual Attempt: WordNet
An Answer (?): Word Embeddings!
Distributional vs. Distributed Representations
Distributional Representations (see Goldberg 10.4.1)
Word Embeddings from Language Models
Context Window Methods
CBOW (Mikolov et al. 2013) • Predict word based on sum of surrounding embeddings
Glove (Pennington et al. 2014)
What Contexts?
Types of Evaluation
Intrinsic Evaluation of Embeddings (categorization from Schnabel et al 2015)
Extrinsic Evaluation: Using Word Embeddings in Systems
How Do I Choose Embeddings?
Limitations of Embeddings
Sub-word Embeddings (2)
Taught by
Graham Neubig
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Neural Nets for NLP 2021 - Distributional Semantics and Word Vectors
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Neural Nets for NLP 2019 - Word Vectors
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CMU Neural Nets for NLP 2018 - Models of Words
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Neural Nets for NLP 2017 - A Simple Exercise - Predicting the Next Word in a Sentence
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Neural Nets for NLP 2021 - Neural Nets + Knowledge Bases
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Neural Nets for NLP 2018 - Learning from-for Knowledge Graphs
