Overview
Syllabus
Intro
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!
Word Embeddings are Cool! (An Obligatory Slide)
Distributional vs. Distributed Representations
Distributional Representations (see Goldberg 10.4.1)
Count-based Methods
Prediction-basd Methods (See Goldberg 10.4.2)
Word Embeddings from Language Models giving
Context Window Methods
Glove (Pennington et al. 2014)
What Contexts?
Types of Evaluation
Non-linear Projection • Non-linear projections group things that are close in high
t-SNE Visualization can be Misleading! Wattenberg et al. 2016
Intrinsic Evaluation of Embeddings (categorization from Schnabel et al 2015)
Extrinsic Evaluation
How Do I Choose Embeddings?
When are Pre-trained Embeddings Useful?
Limitations of Embeddings
Unsupervised Coordination of Embeddings
Retrofitting of Embeddings to Existing Lexicons . We have an existing lexicon like WordNet, and would like our vectors to match (Faruqui et al. 2015)
Sparse Embeddings
De-biasing Word
FastText Toolkit
Taught by
Graham Neubig