Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Incorporating Structure Into Neural Models For Language Processing PDF full book. Access full book title Incorporating Structure Into Neural Models For Language Processing.
| Author | : Michael Schlichtkrull |
| Publisher | : |
| Total Pages | : 140 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
| Author | : Jie Hao |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2020 |
| Genre | : Computer science |
| ISBN | : |
As the rise in availability of natural language data, the underlying language structures can be better learned and play the important roles in many natural language processing tasks. Although the neural language representation models like Transformer trained on large-scale corpora have achieved amazing performance on different natural language processing (NLP) tasks, how to further incorporate the structural knowledge information is not well explored. In this thesis, we propose to explore the structure modeling for existing powerful neural models of natural language via explicitly and implicitly ways, in order to further boost the performance of the models.We describe three general approaches for incorporating structure information into the Transformer, the state of the art model of many NLP tasks. The first method is mainly based on Recurrent Neural Networks (RNNs) and we propose a novel Attentive Recurrent Networks (ARNs) to introduce the recurrence into Transformer. The second method leverages the RNNs' variants ordered neuron Long short-term memory (ON-LSTM). The third method leverages multi granularity phrases information of the sequences, which enables Transformer to capture different segments structure from words to phrases. The linguistic representations learned as a result of structure modeling are shown to be effective across a range of downstream tasks such as neural machine translation (NMT) and text classification. We validate our approaches across a range of tasks, including machine translation, targeted linguistic evaluation, language modeling and logical inference. While machine translation is a benchmark task for deep learning models, the other tasks focus on evaluating how much structure information is encoded in the learned representations and how it can affect models. Experimental results show that the proposed approach consistently improves performances in all tasks, and modeling structure is indeed an essential method for further improving the performance of the NLP models such as Transformer. Furthermore, in the last part of the thesis, we conduct a series of experiments to analyze the importance of syntax information in NLP tasks. In detail, we investigate the role of syntax in NMT and language modeling. More specific, we adopt the On-Lstm decoder, which can be used to induce the latent structure of natural language, to integrate the syntax information into the state-of-the-art Transformer model. Then, by conducting fluency and adequacy evaluation experiments, we illustrate the role of the syntax information in such tasks. Our analysis shade the lights on the role of syntax for NLP tasks especially for the sentence generation in machine translation.
| Author | : S., Sumathi |
| Publisher | : IGI Global |
| Total Pages | : 227 |
| Release | : 2019-11-29 |
| Genre | : Computers |
| ISBN | : 1799811611 |
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 | : Karthiek Reddy Bokka |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 372 |
| Release | : 2019-06-11 |
| Genre | : Computers |
| ISBN | : 1838553673 |
Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key FeaturesGain insights into the basic building blocks of natural language processingLearn how to select the best deep neural network to solve your NLP problemsExplore convolutional and recurrent neural networks and long short-term memory networksBook Description Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues. What you will learnUnderstand various pre-processing techniques for deep learning problemsBuild a vector representation of text using word2vec and GloVeCreate a named entity recognizer and parts-of-speech tagger with Apache OpenNLPBuild a machine translation model in KerasDevelop a text generation application using LSTMBuild a trigger word detection application using an attention modelWho this book is for If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.
| Author | : Pengfei Sun |
| Publisher | : |
| Total Pages | : 230 |
| Release | : 2017 |
| Genre | : Natural language processing (Computer science) |
| ISBN | : |
Neural networks based deep learning methods have gained significant success in several real world tasks: from machine translation to web recommendation, and it is also greatly improving the computer vision and the natural language processing. Compared with conventional machine learning techniques, neural network based deep learning do not require careful engineering and consideration domain expertise to design a feature extractor that transformed the raw data to a suitable internal representation. Its extreme efficacy on multiple levels of representation and feature learning ensures this type of approaches can process high dimensional data. It integrates the feature representation, learning and recognition into a systematical framework, which allows the learning starts at one level (i.e., being with raw input) and end at a higher slightly more abstract level. By simply stacking enough such transformations, very complex functions can be obtained. In general, high level feature representation facilitate the discrimination of patterns, and additionally can reduce the impact of irrelevant variations. However, previous studies indicate that deep composition of the networks make the training errors become vanished. To overcome this weakness, several techniques have been developed, for instance, dropout, stochastic gradient decent and residual network structures. In this study, we incorporates latent information into different network structures (e.g., restricted Boltzmann machine, recursive neural networks, and long short term memory). The conditional latent information reflects the high dimensional correlation existed in the data structure, and the typical network structure may not learn this kind of features due to limitation of the initial design (i.e., the network size the parameters). Similarly to residual nets, the conditional neural networks jointly learns the global features and local features, and the specifically designed network structure helps to incorporate the modulation derived from the probability distribution. The proposed models have been widely tested in different datasets, for instance, the conditional RBM has been applied to detect the speech components, and a language model based gated RBM has been used to recognize speech related EEG patterns. The conditional RNN has been tested in both general natural language modeling and medical notes prediction tasks. The results indicate that by introducing conditional branches in the conventional network structures, the latent features can be globally and locally learned.
| Author | : Palash Goyal |
| Publisher | : Apress |
| Total Pages | : 290 |
| Release | : 2018-06-26 |
| Genre | : Computers |
| ISBN | : 1484236858 |
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 | : Zhiyuan Liu |
| Publisher | : Springer Nature |
| Total Pages | : 319 |
| Release | : 2020-07-03 |
| Genre | : Computers |
| ISBN | : 9811555737 |
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
| Author | : Gerhard Paaß |
| Publisher | : Springer Nature |
| Total Pages | : 448 |
| Release | : 2023-05-23 |
| Genre | : Computers |
| ISBN | : 3031231902 |
This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.
| Author | : Nicola De Cao |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2024 |
| Genre | : |
| ISBN | : |
"This thesis explores how to enhance natural language understanding by incorporating entity information into neural network models. It tackles three key questions:1. Leveraging entities for understanding tasks: This work introduces Entity-GCN, a model that performs multi-step reasoning on a graph where nodes represent entity mentions and edges represent relationships. This method achieved state-of-the-art results on a multi-document question-answering dataset.2. Identifying and disambiguating entities using large language models: This research proposes a novel system that retrieves entities by generating their names token-by-token, overcoming limitations of traditional methods and significantly reducing memory footprint. This approach is also extended to a multilingual setting and further optimized for efficiency.3. Interpreting and controlling entity knowledge within models: This thesis presents a post-hoc interpretation technique to analyze how decisions are made across layers in neural models, allowing for visualization and analysis of knowledge representation. Additionally, a method for editing factual knowledge about entities is proposed, enabling correction of model predictions without costly retraining."--
| Author | : Denis Rothman |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 385 |
| Release | : 2021-01-29 |
| Genre | : Computers |
| ISBN | : 1800568630 |
Publisher's Note: A new edition of this book is out now that includes working with GPT-3 and comparing the results with other models. It includes even more use cases, such as casual language analysis and computer vision tasks, as well as an introduction to OpenAI's Codex. Key FeaturesBuild and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning modelsGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machineTest transformer models on advanced use casesBook Description The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets. What you will learnUse the latest pretrained transformer modelsGrasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer modelsCreate language understanding Python programs using concepts that outperform classical deep learning modelsUse a variety of NLP platforms, including Hugging Face, Trax, and AllenNLPApply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and moreMeasure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is for Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data.
