num_pages: 309
description: Table of Contents:PrefaceAcknowledgmentsIntroductionLearning Basics and Linear ModelsFrom Linear Models to Multi-layer PerceptronsFeed-forward Neural NetworksNeural Network TrainingFeatures for Textual DataCase Studies of NLP FeaturesFrom Textual Features to InputsLanguage ModelingPre-trained Word RepresentationsUsing Word EmbeddingsCase Study: A Feed-forward Architecture for Sentence Meaning InferenceNgram Detectors: Convolutional Neural NetworksRecurrent Neural Networks: Modeling Sequences and StacksConcrete Recurrent Neural Network ArchitecturesModeling with Recurrent NetworksConditioned GenerationModeling Trees with Recursive Neural NetworksStructured Output PredictionCascaded Multi-task and Semi-supervised LearningConclusionBibliographyAuthor's Biography
language: English
title: Neural Network Methods for Natural Language Processing
publisher: Morgan & Claypool