000			01840nam a22002657a 4500
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005			20241129121311.0
008			241125b |||||||| |||| 00| 0 eng d
020			_a9783031010378
040			_cAKCL
082			_a006.32 _bGOL-N
100			_aGoldberg, Yoav
245			_aNeural Network Methods for Natural Language Processing / _cby Yoav Goldberg
260			_aSwitzerland: _b Springer Nature, _c2022
300			_ap. XXII,287
520			_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.
650			_aComputer Science
650			_aArtificial Intelligence
650			_aNeural Networks
650			_aNatural Language Processing
650			_aMachine Learning
942			_cREF
942			_2ddc
942			_2ddc _n0
999			_c39993 _d39993