Neural Network Methods in Natural Language Processing (Synthesis Lectures on Human Language Technologies)

Yoav Goldberg

  • 出版商: Morgan & Claypool
  • 出版日期: 2017-04-17
  • 售價: $2,250
  • 貴賓價: 9.5$2,138
  • 語言: 英文
  • 頁數: 310
  • 裝訂: Paperback
  • ISBN: 1627052984
  • ISBN-13: 9781627052986
  • 相關分類: Text-miningDeepLearning
  • 立即出貨 (庫存=1)

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商品描述

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.

商品描述(中文翻譯)

神經網絡是一系列強大的機器學習模型。本書專注於將神經網絡模型應用於自然語言數據。書的前半部分(第一部分和第二部分)介紹了監督式機器學習和前饋神經網絡的基礎知識,以及處理語言數據的基礎知識,並使用基於向量而非符號表示的詞語。它還介紹了計算圖抽象,該抽象使得能夠輕鬆定義和訓練任意神經網絡,並且是當代神經網絡軟件庫設計的基礎。

書的後半部分(第三部分和第四部分)介紹了更專門的神經網絡架構,包括一維卷積神經網絡、循環神經網絡、條件生成模型和注意力模型。這些架構和技術是機器翻譯、句法分析和許多其他應用的最先進算法的驅動力。最後,我們還討論了樹狀網絡、結構化預測和多任務學習的前景。