| 紀錄類型: |
書目-電子資源
: Monograph/item
|
| 正題名/作者: |
Neural network methods for natural language processing // Yoav Goldberg |
| 作者: |
Goldberg, Yoav, |
| 面頁冊數: |
1 online resource (xxii, 287 pages) :illustrations (some color) |
| 內容註: |
1. Introduction -- 1.1 The challenges of natural language processing -- 1.2 Neural networks and deep learning -- 1.3 Deep learning in NLP -- 1.3.1 Success stories -- 1.4 Coverage and organization -- 1.5 What's not covered -- 1.6 A note on terminology -- 1.7 Mathematical notation -- |
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Part I. Supervised classification and feed-forward neural networks -- 2. Learning basics and linear models -- 2.1 Supervised learning and parameterized functions -- 2.2 Train, test, and validation sets -- 2.3 Linear models -- 2.3.1 Binary classification -- 2.3.2 Log-linear binary classification -- 2.3.3 Multi-class classification -- 2.4 Representations -- 2.5 One-hot and dense vector representations -- 2.6 Log-linear multi-class classification -- 2.7 Training as optimization -- 2.7.1 Loss functions -- 2.7.2 Regularization -- 2.8 Gradient-based optimization -- 2.8.1 Stochastic gradient descent -- 2.8.2 Worked -out example -- 2.8.3 Beyond SGD -- |
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3. From linear models to multi-layer perceptrons -- 3.1 Limitations of linear models: The XOR problem -- 3.2 Nonlinear input transformations -- 3.3 Kernel methods -- 3.4 Trainable mapping functions -- |
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4. Feed-forward neural networks -- 4.1 A brain-inspired metaphor -- 4.2 In mathematical notation -- 4.3 Representation power -- 4.4 Common nonlinearities -- 4.5 Loss functions -- 4.6 Regularization and dropout -- 4.7 Similarity and distance layers -- 4.8 Embedding layers -- |
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5. Neural network training -- 5.1 The computation graph abstraction -- 5.1.1 Forward computation -- 5.1.2 Backward computation (derivatives, backprop) -- 5.1.3 Software -- 5.1.4 Implementation recipe -- 5.1.5 Network composition -- 5.2 Practicalities -- 5.2.1 Choice of optimization algorithm -- 5.2.2 Initialization -- 5.2.3 Restarts and ensembles -- 5.2.4 Vanishing and exploding gradients -- 5.2.5 Saturation and dead neurons -- 5.2.6 Shuffling -- 5.2.7 Learning rate -- 5.2.8 Minibatches -- |
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Part II. Working with natural language data -- 6. Features for textual data -- 6.1 Typology of NLP classification problems -- 6.2 Features for NLP problems -- 6.2.1 Directly observable properties -- 6.2.2 Inferred linguistic properties -- 6.2.3 Core features vs. combination features -- 6.2.4 Ngram features -- 6.2.5 Distributional features -- |
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7. Case studies of NLP features -- 7.1 Document classification: language identification -- 7.2 Document classification: topic classification -- 7.3 Document classification: authorship attribution -- 7.4 Word-in-context: part of speech tagging -- 7.5 Word-in-context: named entity recognition -- 7.6 Word in context, linguistic features: preposition sense disambiguation -- 7.7 Relation between words in context: arc-factored parsing -- |
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8. From textual features to inputs -- 8.1 Encoding categorical features -- 8.1.1 One-hot encodings -- 8.1.2 Dense encodings (feature embeddings) -- 8.1.3 Dense vectors vs. one-hot representations -- 8.2 Combining dense vectors -- 8.2.1 Window- based features -- 8.2.2 Variable number of features: continuous bag of words -- 8.3 Relation between one-hot and dense vectors -- 8.4 Odds and ends -- 8.4.1 Distance and position features -- 8.4.2 Padding, unknown words, and word dropout -- 8.4.3 Feature combinations -- 8.4.4 Vector sharing -- 8.4.5 Dimensionality -- 8.4.6 Embeddings vocabulary -- 8.4.7 Network's output -- 8.5 Example: part-of-speech tagging -- 8.6 Example: arc-factored parsing -- |
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9. Language modeling -- 9.1 The language modeling task -- 9.2 Evaluating language models: perplexity -- 9.3 Traditional approaches to language modeling -- 9.3.1 Further reading -- 9.3.2 Limitations of traditional language models -- 9.4 Neural language models -- 9.5 Using language models for generation -- 9.6 Byproduct: word representations -- |
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10. Pre-trained word representations -- 10.1 Random initialization -- 10.2 Supervised task-specific pre-training -- 10.3 Unsupervised pre-training -- 10.3.1 Using pre-trained embeddings -- 10.4 Word embedding algorithms -- 10.4.1 Distributional hypothesis and word representations -- 10.4.2 From neural language models to distributed representations -- 10.4.3 Connecting the worlds -- 10.4.4 Other algorithms -- 10.5 The choice of contexts -- 10.5.1 Window approach -- 10.5.2 Sentences, paragraphs, or documents -- 10.5.3 Syntactic window -- 10.5.4 Multilingual -- 10.5.5 Character-based and sub-word representations -- 10.6 Dealing with multi-word units and word inflections -- 10.7 Limitations of distributional methods -- |
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11. Using word embeddings -- 11.1 Obtaining word vectors -- 11.2 Word similarity -- 11.3 Word clustering -- 11.4 Finding similar words -- 11.4.1 Similarity to a group of words -- 11.5 Odd-one out -- 11.6 Short document similarity -- 11.7 Word analogies -- 11.8 Retrofitting and projections -- 11.9 Practicalities and pitfalls -- |
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12. Case study: a feed-forward architecture for sentence meaning inference -- 12.1 Natural language inference and the SNLI dataset -- 12.2 A textual similarity network -- |
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Part III. Specialized architectures -- 13. Ngram detectors: convolutional neural networks -- 13.1 Basic convolution + pooling -- 13.1.1 1D convolutions over text -- 13.1.2 Vector pooling -- 13.1.3 Variations -- 13.2 Alternative: feature hashing -- 13.3 Hierarchical convolutions -- |
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14. Recurrent neural networks: modeling sequences and stacks -- 14.1 The RNN abstraction -- 14.2 RNN training -- 14.3 Common RNN usage-patterns -- 14.3.1 Acceptor -- 14.3.2 Encoder -- 14.3.3 Transducer -- 14.4 Bidirectional RNNs (biRNN) -- 14.5 Multi-layer (stacked) RNNs -- 14.6 RNNs for representing stacks -- 14.7 A note on reading the literature -- |
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15. Concrete recurrent neural network architectures -- 15.1 CBOW as an RNN -- 15.2 Simple RNN -- 15.3 Gated architectures -- 15.3.1 LSTM -- 15.3.2 GRU -- 15.4 Other variants -- 15.5 Dropout in RNNs -- |
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16. Modeling with recurrent networks -- 16.1 Acceptors -- 16.1.1 Sentiment classification -- 16.1.2 Subject-verb agreement grammaticality detection -- 16.2 RNNs as feature extractors -- 16.2.1 Part-of-speech tagging -- 16.2.2 RNN-CNN document classification -- 16.2.3 Arc-factored dependency parsing -- |
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17. Conditioned generation -- 17.1 RNN generators -- 17.1.1 Training generators -- 17.2 Conditioned generation (encoder- decoder) -- 17.2.1 Sequence to sequence models -- 17.2.2 Applications -- 17.2.3 Other conditioning contexts -- 17.3 Unsupervised sentence similarity -- 17.4 Conditioned generation with attention -- 17.4.1 Computational complexity -- 17.4.2 Interpretability -- 17.5 Attention-based models in NLP -- 17.5.1 Machine translation -- 17.5.2 Morphological inflection -- 17.5.3 Syntactic parsing -- |
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Part IV. Additional topics -- 18. Modeling trees with recursive neural networks -- 18.1 Formal definition -- 18.2 Extensions and variations -- 18.3 Training recursive neural networks -- 18.4 A simple alternative-linearized trees -- 18.5 Outlook -- |
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19. Structured output prediction -- 19.1 Search-based structured prediction -- 19.1.1 Structured prediction with linear models -- 19.1.2 Nonlinear structured prediction -- 19.1.3 Probabilistic objective (CRF) -- 19.1.4 Approximate search -- 19.1.5 Reranking -- 19.1.6 See also -- 19.2 Greedy structured prediction -- 19.3 Conditional generation as structured output prediction -- 19.4 Examples -- 19.4.1 Search-based structured prediction: first-order dependency parsing -- 19.4.2 Neural-CRF for named entity recognition -- 19.4.3 Approximate NER-CRF with beam-search -- |
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20. Cascaded, multi-task and semi-supervised learning -- 20.1 Model cascading -- 20.2 Multi-task learning -- 20.2.1 Training in a multi-task setup -- 20.2.2 Selective sharing -- 20.2.3 Word- embeddings pre-training as multi-task learning -- 20.2.4 Multi- task learning in conditioned generation -- 20.2.5 Multi-task learning as regularization -- 20.2.6 Caveats -- 20.3 Semi- supervised learning -- 20.4 Examples -- 20.4.1 Gaze-prediction and sentence compression -- 20.4.2 Arc labeling and syntactic parsing -- 20.4.3 Preposition sense disambiguation and preposition translation prediction -- 20.4.4 Conditioned generation: multilingual machine translation, parsing, and image captioning -- 20.5 Outlook -- |
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21. Conclusion -- 21.1 What have we seen? -- 21.2 The challenges ahead -- Bibliography -- Author's biography |
| 標題: |
Natural language processing (Computer science) - |
| 電子資源: |
http://portal.igpublish.com/iglibrary/search/MCPB0000900.html |
| ISBN: |
9781627052955 |