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Distributional Representations (see Goldberg 10.4.1)
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Classroom Contents
Neural Nets for NLP 2021 - Distributional Semantics and Word Vectors
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- 1 Intro
- 2 Remember: Neural Models
- 3 How to Train Embeddings?
- 4 What do we want to know about words?
- 5 Contextualization of Word Representations
- 6 A Manual Attempt: WordNet
- 7 An Answer (?): Word Embeddings!
- 8 Word Embeddings are Cool! (An Obligatory Slide)
- 9 Distributional vs. Distributed Representations
- 10 Distributional Representations (see Goldberg 10.4.1)
- 11 Count-based Methods
- 12 Prediction-basd Methods (See Goldberg 10.4.2)
- 13 Word Embeddings from Language Models giving
- 14 Context Window Methods
- 15 Glove (Pennington et al. 2014)
- 16 What Contexts?
- 17 Types of Evaluation
- 18 Non-linear Projection • Non-linear projections group things that are close in high
- 19 t-SNE Visualization can be Misleading! Wattenberg et al. 2016
- 20 Intrinsic Evaluation of Embeddings (categorization from Schnabel et al 2015)
- 21 Extrinsic Evaluation
- 22 How Do I Choose Embeddings?
- 23 When are Pre-trained Embeddings Useful?
- 24 Limitations of Embeddings
- 25 Unsupervised Coordination of Embeddings
- 26 Retrofitting of Embeddings to Existing Lexicons . We have an existing lexicon like WordNet, and would like our vectors to match (Faruqui et al. 2015)
- 27 Sparse Embeddings
- 28 De-biasing Word
- 29 FastText Toolkit
