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Author: Manh-Ke Tran
Publisher:
ISBN: 9789463752084
Category :
Languages : en
Pages : 138
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Book Description
In the field of natural language processing (NLP), recent research has shown that deep neural network models are quite brittle and can only model linguistic principles to a limited degree. In order to build NLP systems that can generalize and work well in practice, it is important to integrate linguistic knowledge into such systems as well as investigate the ability of current models in capturing linguistic phenomena. This thesis attempts to address those two aspects from four different angles. First, this thesis demonstrates that neural network models enable the integration of morphological knowledge seamlessly into phrase-based machine translation systems without any feature engineering. Second, this thesis investigates what linguistic phenomena are implicitly captured by recurrent neural networks by augmenting them with an external memory. This thesis also studies the impact of recurrent vs non-recurrent architectures in modeling hierarchical structure. Third, while neural networks are well known to be powerful supervised learners, this thesis investigates whether they offer the same benefits for unsupervised structure learning. This thesis proposes an unsupervised Neural Hidden Markov Model for the purpose of part-of-speech induction. Finally, this thesis asks whether neural networks can induce meaningful structure from non-annotated text. This thesis proposes structured attention models that induce a dependency-like tree representation of the input sentence for the purpose of translation and shows that the models learn some basic elements of the source language grammar.
Author: Manh-Ke Tran
Publisher:
ISBN: 9789463752084
Category :
Languages : en
Pages : 138
Get Book Here
Book Description
In the field of natural language processing (NLP), recent research has shown that deep neural network models are quite brittle and can only model linguistic principles to a limited degree. In order to build NLP systems that can generalize and work well in practice, it is important to integrate linguistic knowledge into such systems as well as investigate the ability of current models in capturing linguistic phenomena. This thesis attempts to address those two aspects from four different angles. First, this thesis demonstrates that neural network models enable the integration of morphological knowledge seamlessly into phrase-based machine translation systems without any feature engineering. Second, this thesis investigates what linguistic phenomena are implicitly captured by recurrent neural networks by augmenting them with an external memory. This thesis also studies the impact of recurrent vs non-recurrent architectures in modeling hierarchical structure. Third, while neural networks are well known to be powerful supervised learners, this thesis investigates whether they offer the same benefits for unsupervised structure learning. This thesis proposes an unsupervised Neural Hidden Markov Model for the purpose of part-of-speech induction. Finally, this thesis asks whether neural networks can induce meaningful structure from non-annotated text. This thesis proposes structured attention models that induce a dependency-like tree representation of the input sentence for the purpose of translation and shows that the models learn some basic elements of the source language grammar.
Author: Noah A. Smith
Publisher: Springer Nature
ISBN: 3031021436
Category : Computers
Languages : en
Pages : 248
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Book Description
A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference
Author: Shalom Lappin
Publisher: CRC Press
ISBN: 1000380327
Category : Computers
Languages : en
Pages : 162
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Book Description
The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.
Author: Amit Kumar Tyagi
Publisher: CRC Press
ISBN: 1000626164
Category : Technology & Engineering
Languages : en
Pages : 413
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Book Description
The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding. FEATURES Covers computational analysis and understanding of natural languages Discusses applications of recurrent neural network in e-Healthcare Provides case studies in every chapter with respect to real-world scenarios Examines open issues with natural language, health care, multimedia (Audio/Video), transportation, stock market, and logistics The text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology.
Author: S., Sumathi
Publisher: IGI Global
ISBN: 1799811611
Category : Computers
Languages : en
Pages : 227
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Book Description
Information in today’s advancing world is rapidly expanding and becoming widely available. This eruption of data has made handling it a daunting and time-consuming task. Natural language processing (NLP) is a method that applies linguistics and algorithms to large amounts of this data to make it more valuable. NLP improves the interaction between humans and computers, yet there remains a lack of research that focuses on the practical implementations of this trending approach. Neural Networks for Natural Language Processing is a collection of innovative research on the methods and applications of linguistic information processing and its computational properties. This publication will support readers with performing sentence classification and language generation using neural networks, apply deep learning models to solve machine translation and conversation problems, and apply deep structured semantic models on information retrieval and natural language applications. While highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and development professionals, IT specialists, industrialists, technology developers, data analysts, data scientists, academics, researchers, and students seeking current research on the fundamental concepts and techniques of natural language processing.
Author: Lyndon White
Publisher: Springer
ISBN: 9811300623
Category : Technology & Engineering
Languages : en
Pages : 132
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Book Description
This book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas – as Webster’s 1923 “English Composition and Literature” puts it: “A sentence is a group of words expressing a complete thought”. Thus the representation of sentences and the words that make them up is vital in advancing artificial intelligence and other “smart” systems currently being developed. Providing an overview of the research in the area, from Bengio et al.’s seminal work on a “Neural Probabilistic Language Model” in 2003, to the latest techniques, this book enables readers to gain an understanding of how the techniques are related and what is best for their purposes. As well as a introduction to neural networks in general and recurrent neural networks in particular, this book details the methods used for representing words, senses of words, and larger structures such as sentences or documents. The book highlights practical implementations and discusses many aspects that are often overlooked or misunderstood. The book includes thorough instruction on challenging areas such as hierarchical softmax and negative sampling, to ensure the reader fully and easily understands the details of how the algorithms function. Combining practical aspects with a more traditional review of the literature, it is directly applicable to a broad readership. It is an invaluable introduction for early graduate students working in natural language processing; a trustworthy guide for industry developers wishing to make use of recent innovations; and a sturdy bridge for researchers already familiar with linguistics or machine learning wishing to understand the other.
Author: Yusuke Sugomori
Publisher: Packt Publishing Ltd
ISBN: 1788471717
Category : Computers
Languages : en
Pages : 744
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Book Description
Build and run intelligent applications by leveraging key Java machine learning libraries About This Book Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries. Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applications This step-by-step guide will help you solve real-world problems and links neural network theory to their application Who This Book Is For This course is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life. What You Will Learn Get a practical deep dive into machine learning and deep learning algorithms Explore neural networks using some of the most popular Deep Learning frameworks Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms Apply machine learning to fraud, anomaly, and outlier detection Experiment with deep learning concepts, algorithms, and the toolbox for deep learning Select and split data sets into training, test, and validation, and explore validation strategies Apply the code generated in practical examples, including weather forecasting and pattern recognition In Detail Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognitionStarting with an introduction to basic machine learning algorithms, this course takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. This course helps you solve challenging problems in image processing, speech recognition, language modeling. You will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text. You will also work with examples such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning and more. By the end of this course, you will have all the knowledge you need to perform deep learning on your system with varying complexity levels, to apply them to your daily work. The course provides you with highly practical content explaining deep learning with Java, from the following Packt books: Java Deep Learning Essentials Machine Learning in Java Neural Network Programming with Java, Second Edition Style and approach This course aims to create a smooth learning path that will teach you how to effectively use deep learning with Java with other de facto components to get the most out of it. Through this comprehensive course, you'll learn the basics of predictive modelling and progress to solve real-world problems and links neural network theory to their application
Author: Palash Goyal
Publisher: Apress
ISBN: 1484236858
Category : Computers
Languages : en
Pages : 290
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Book Description
Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.
Author: Jinman Zhao
Publisher:
ISBN:
Category :
Languages : en
Pages : 138
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Book Description
Naturalness, or statistical patterns, originated and first observed for natural languages, has been the fundamental assumption for machine learning approaches for natural language processing. Although seemingly different, recent studies have suggested that computer software, in the form of source code, also observes naturalness property similar to that of natural language text, and thus have given rise to machine learning approaches applied to the domain of code. Both source code and natural language are highly structural and compositional. However, solutions that currently prevail in machine learning, such as neural networks, do not typically assume structural or compositional inputs. This dissertation expands the usage of structures and compositions in learning naturalness from source code and natural language. We propose ways to bridge this gap in various scenarios for code and language, which leads to more efficient learning systems and deeper understandings in dealing with structural and compositional input. We first propose type-directed encoders (TDE) for systematically designing and comparing neural solutions in improving the precision of static analysis results. TDE is a general framework for constructing encoders of a compound data type by recursively composing encoders for its constituent types. We focus on the problem of discovering communication links between applications in the popular Android mobile operating system. We use TDE as a crucial part of a neural network architecture that encodes abstractions of communicating objects and estimates the probability with which a link indeed exists. Evaluations over a large corpus of Android applications demonstrate that TDE helped design neural architectures that achieve high predictive power. We further conduct thorough interpretability studies to understand the internals of the learned neural networks. We then look into the benefit of utilizing structures for code completion. We advance the accuracy of code next token prediction using Transformers as the base neural architecture and device ways of communicating the syntactic structure of code to the Transformer. Comprehensive experimental evaluations of our proposal, along with alternative design choices, show that our structure-aware variations further increase the margin by which a Transformer-based system outperforms previous systems. Inspections reveal that our structure-aware model attends meaningful locations in predicting the next token. For natural language, we utilize the composition of words and propose the bag-of-subwords (BoS) model for generating word vector representations (embeddings) from words' spelling. The model is simple, fast, yet effective in generalizing pre-trained word embeddings towards out-of-vocabulary words. Experiments of word similarity in English and the joint prediction of part-of-speech tag and morphosyntactic attributes in23 languages support the claim. Based on BoS, we further propose the probabilistic bag-of-subwords (PBoS) model that simultaneously models subword segmentations and composes subword-based word embeddings. With an efficient algorithm, we apply bag-of-subwords for all possible segmentations and assign weights to sub-words based on their likelihood. Inspections and affix prediction results show that PBoS can produce meaningful subword segmentations and subword rankings without any source of explicit morphological knowledge. Word similarity and POS tagging results show clear advantages of PBoS over previous subword-level models in the quality of generated word embeddings across languages.
Author: Li Deng
Publisher: Springer
ISBN: 9811052093
Category : Computers
Languages : en
Pages : 338
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Book Description
In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.
