Neural Network Methods in Natural Language Processing

Ön Kapak
Morgan & Claypool Publishers, 17 Nis 2017 - 309 sayfa

Neural networks are a family of powerful machine learning models and this book focuses on their application 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.

 

Seçilmiş sayfalar

İçindekiler

Preface
Conditioned Generation
Introduction
Learning Basics and Linear Models
From Linear Models to Multilayer Perceptrons
Neural Network Training
Features for Textual Data
Case Studies of NLP Features
Pretrained Word Representations
Using Word Embeddings
A Feedforward Architecture for Sentence Meaning Inference
Convolutional Neural Networks
Modeling Sequences and Stacks
Modeling with Recurrent Networks
Modeling Trees with Recursive Neural Networks
Structured Output Prediction

From Textual Features to Inputs
Language Modeling
Telif Hakkı

Diğer baskılar - Tümünü görüntüle

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Tümünü görüntüle »

Sık kullanılan terimler ve kelime öbekleri

activation function algorithm annotated approach architecture assign Association for Computational bag-of-words Bengio bigrams binary biRNN capture CBOW Chapter classification Collobert combination common computation graph Computational Linguistics concatenation consider context convolution layers core features corpus corresponding dataset decoder defined dimensions discussed distributional document document classification dropout embedding matrix embedding vector encoding feed-forward given Goldberg gradient-based gradients k-gram label language modeling learning rate linear classifier linear models loss function LSTM machine learning machine translation mapping methods Mikolov Natural Language Processing neuron ngrams node nonlinear noun one-hot optimization pairs parameters parser part-of-speech tagging POS-tags pre-trained word preposition problem Proc recurrent neural networks represent resulting rows score Section semantic sentence sequence of words similar softmax supervised symbol syntactic task techniques training data training examples training set transformation tree values verb vocabulary w₁ window word at position word embeddings word representations word vectors word-context WORD2VEC x₁

Yazar hakkında (2017)

Bar Ilan University

University of Toronto

Kaynakça bilgileri

BaşlıkNeural Network Methods in Natural Language Processing
Synthesis Lectures on Human Language Technologies
YazarYoav Goldberg
Katkıda BulunanGraeme Hirst
YayıncıMorgan & Claypool Publishers, 2017
ISBN168173155X, 9781681731551
Uzunluk309 sayfa
  
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