Neural network methods for natural language processing / [electronic resource]

• 作者： Goldberg, Yoav, 1980- author
• 其他題名：
  • Synthesis lectures on human language technologies ;
• 出版： [San Rafael, California] : Morgan & Claypool Publishers
• 叢書名： Synthesis lectures on human language technologies,#37
• 主題： Natural language processing (Computer science) , Neural networks (Computer science) , natural language processing , machine learning , supervised learning , deep learning , neural networks , word embeddings , recurrent neural networks , sequence to sequence models , Electronic books
• ISBN： 9781627052955 (electronic bk.) 、 162705295X (electronic bk.)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Includes bibliographical references (pages 253-285) 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 21. Conclusion -- 21.1 What have we seen? -- 21.2 The challenges ahead -- Bibliography -- Author's biography
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• 摘要註: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models These architectures and techniques are the driving force behind state- of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning
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