| 000 | 12078nam a2200865 i 4500 | ||
|---|---|---|---|
| 001 | 7909255 | ||
| 003 | IEEE | ||
| 005 | 20200413152924.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 170418s2017 caua foab 000 0 eng d | ||
| 020 |
_a9781627052955 _qebook |
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| 020 |
_z9781627052986 _qprint |
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| 024 | 7 |
_a10.2200/S00762ED1V01Y201703HLT037 _2doi |
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| 035 | _a(CaBNVSL)swl00407294 | ||
| 035 | _a(OCoLC)982699889 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aQA76.9.N38 _bG655 2017 |
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| 082 | 0 | 4 |
_a006.35 _223 |
| 100 | 1 |
_aGoldberg, Yoav, _eauthor. |
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| 245 | 1 | 0 |
_aNeural network methods for natural language processing / _cYoav Goldberg. |
| 264 | 1 |
_a[San Rafael, California] : _bMorgan & Claypool, _c2017. |
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| 300 |
_a1 PDF (xxii, 287 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 |
_aSynthesis lectures on human language technologies, _x1947-4059 ; _v# 37 |
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| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 538 | _aMode of access: World Wide Web. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 504 | _aIncludes bibliographical references (pages 253-285). | ||
| 505 | 8 | _a21. Conclusion -- 21.1 What have we seen? -- 21.2 The challenges ahead -- Bibliography -- Author's biography. | |
| 505 | 8 | _a20. 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 -- | |
| 505 | 8 | _a19. 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 -- | |
| 505 | 8 | _aPart 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 -- | |
| 505 | 8 | _a17. 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 -- | |
| 505 | 8 | _a16. 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 -- | |
| 505 | 8 | _a15. 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 -- | |
| 505 | 8 | _aPart 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 -- | |
| 505 | 8 | _a12. 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 -- | |
| 505 | 8 | _a11. 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 -- | |
| 505 | 8 | _a10. 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 -- | |
| 505 | 8 | _a9. 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 -- | |
| 505 | 8 | _a8. 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 -- | |
| 505 | 8 | _a7. 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 -- | |
| 505 | 8 | _aPart 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 -- | |
| 505 | 8 | _a5. 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 -- | |
| 505 | 8 | _a4. 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 -- | |
| 505 | 8 | _a3. 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 -- | |
| 505 | 8 | _aPart 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 -- | |
| 505 | 8 | _a14. 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 -- | |
| 505 | 0 | _a1. 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 -- | |
| 506 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | ||
| 510 | 0 | _aGoogle book search | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aCompendex | |
| 520 | 3 | _aNeural 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. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on April 18, 2017). | ||
| 650 | 0 | _aNeural networks (Computer science) | |
| 650 | 0 | _aNatural language processing (Computer science) | |
| 653 | _asequence to sequence models | ||
| 653 | _arecurrent neural networks | ||
| 653 | _aword embeddings | ||
| 653 | _aneural networks | ||
| 653 | _adeep learning | ||
| 653 | _asupervised learning | ||
| 653 | _amachine learning | ||
| 653 | _anatural language processing | ||
| 776 | 0 | 8 |
_iPrint version: _z9781627052986 |
| 830 | 0 |
_aSynthesis lectures on human language technologies ; _v# 37. _x1947-4059 |
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| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=7909255 |
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_c562258 _d562258 |
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