A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for Nlp, Gomez-Perez Jose Manuel, Denaux Ronald, Garcia-Silva Andres
Описание: The Natural Language Processing Workshop takes you through fundamental NLP techniques, such as preparing datasets, collecting text, extracting text, and sentiment analysis. As you progress, you`ll get to grips with creating your own chatbots and dynamic models.
Автор: Goldberg Yoav Название: Neural Network Methods in Natural Language Processing ISBN: 1627052984 ISBN-13(EAN): 9781627052986 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 11504.00 р. Наличие на складе: Поставка под заказ.
Описание: 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.
Описание: The Applied AI and NLP Workshop will show you how to integrate artificial intelligence with Amazon Web Services to create intelligent applications. From developing language translation apps and chatbots to creating models for processing large volumes of images, you`ll learn key concepts effectively and in a real-world context.
Автор: Sri, Mathangi Название: Practical natural language processing with python ISBN: 148426245X ISBN-13(EAN): 9781484262450 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Chapter 1: Text Data in Real Word
Chapter Goal: This chapter focuses on various types of text data. The information it offers and the commercial value that each of the data could potentially offer. Understanding of the data provides the reader the landscape that they are getting into
No of pages: 10
Sub -Topics
NLP
Search
Reviews
Tweets/FB Posts
Chat data
SMS data
Content data
IVR utterance data
Chapter 2: NLP in Customer Service
Chapter Goal: Case studies for problems in customer service and how they could be solved.
No of pages: 39
Sub - Topics
1. A quick overview of the customer service industry
2. Voice Calls
3. Chats.
4. Tickets Data
5. Email Data
6. Voice of customer analysis
7. Intent Mining
8. NPS/CSAT drivers
9. Insights in Sales Chats
10. Reasons for non purchase
11. Survey Comment Analysis
12. Mining Voice transcripts
Chapter 3: NLP in Online Reviews
Chapter Goal: Case studies for problems in online reviews and how they could be solved.
No of pages: 39
Sub - Topics:
1. Sentiment Analysis
2. Emotion Mining
3. Approach 1: Lexicon based approach
4. Approach 2: Rules based approach
5. Approach 3 - Machine Learning based approach (Neural Network)
6. Attribute Extraction
Chapter 4: NLP in BFSI
Chapter Goal: case studies for problems in the banking industry
Sub - Topics:
1. NLP in Fraud
2. Method 1 (For extracting NER, popular libraries)
3. Method 2 (For extracting NER, rules based approach)
4. Method 3 (Classifier based approach using word embeddings and neural networks)
5. Other use cases of NLP in BFSI
6. Natural Language Generation in banks
No of pages: 47
Chapter 5: NLP in Virtual Assistants
Chapter Goal: Case study in building state of the art natural language bots
Sub- Topics
1. Overview
2. Approach 1: The "Classic" approach using LSTMs
3. Approach 2: Generating Responses
4. BERT
5. Further nuances in building conversational bots:
One-stop solution for NLP practitioners, ML developers and data scientists to build effective NLP systems that can perform real-world complicated tasks
Key Features
Implement deep learning algorithms such as BiLSTMS, CRFs, and many more using TensorFlow 2
Explore classical NLP techniques and libraries including parts-of-speech tagging and tokenization
Learn practical applications of NLP covering the forefronts of the field like sentiment analysis and generating text
Book Description
In the last couple of years, there have been tremendous advances in natural language processing, and we are now moving from research labs into practical applications. Advanced Natural Language Processing comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques.
This book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It goes into the details of applying the concepts of text pre-processing using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. Named Entity Recognition (NER), a cornerstone of task-oriented bots, is built from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs.
Taking a practical and application-focused perspective, the book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbot design. It also covers one of the most important reasons behind recent advances in NLP - applying transfer learning and fine-tuning using TensorFlow 2.
Further, it covers practical techniques that can simplify the labelling of textual data which otherwise proves to be a costly affair. The book also has a working code for each tech piece so that you can adapt them to your use cases.
By the end of this TensorFlow book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems.
What You Will Learn
Grasp important pre-steps in building NLP applications like POS tagging
Deal with vast amounts of unlabeled and small labelled Datasets in NLP
Use transfer and weakly supervised learning using libraries like Snorkel
Perform sentiment analysis using BERT
Apply encoder-decoder NN architectures and beam search for summarizing text
Use transformer models with attention to bring images and text together
Build applications that generate captions and answer questions about images
Use advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest deep NLP models
Who this book is for
This is not an introductory book and assumes the reader is familiar with basics of NLP and has fundamental Python skills, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra.
The readers who can benefit the most from this book include:
Intermediate ML developers who are familiar with the basics of supervised learning and deep learning techniques
Professionals who already use TensorFlow/Python for purposes such as data science, ML, research, and analysis
Описание: Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide.
Описание: This volume reports on excavations in advance of the development of a site in Norton-on-Derwent, North Yorkshire close to the line of the main Roman road running from the crossing point of the River Derwent near Malton Roman fort to York. This site provided much additional information on aspects of the poorly understood `small town` of Delgovicia.
Автор: Tecuci Название: Knowledge Engineering ISBN: 1107122562 ISBN-13(EAN): 9781107122567 Издательство: Cambridge Academ Рейтинг: Цена: 11722.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents a significant advancement in knowledge engineering based on learning agent technology. Using the software Disciple-EBR, students, practitioners, and researchers can rapidly develop learning assistants in numerous domains that require evidence-based reasoning, including cyber security, law, forensics, medicine, and education.
Автор: Janev Valentina, Graux Damien, Jabeen Hajira Название: Knowledge Graphs and Big Data Processing ISBN: 3030531988 ISBN-13(EAN): 9783030531980 Издательство: Springer Рейтинг: Цена: 5589.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Foundations.- Chapter 1. Ecosystem of Big Data.- Chapter 2. Knowledge Graphs: The Layered Perspective.- Chapter 3. Big Data Outlook, Tools, and Architectures.- Architecture.- Chapter 4. Creation of Knowledge Graphs.- Chapter 5. Federated Query Processing.- Chapter 6. Reasoning in Knowledge Graphs: An Embeddings Spotlight.- Methods and Solutions.- Chapter 7. Scalable Knowledge Graph Processing using SANSA.- Chapter 8. Context-Based Entity Matching for Big Data.- Applications.- Chapter 9. Survey on Big Data Applications.- Chapter 10. Case Study from the Energy Domain.
Автор: Lotem Peled-Cohen, Roi Reichart, Rotem Dror, Segev Shlomov Название: Statistical Significance Testing for Natural Language Processing ISBN: 1681737973 ISBN-13(EAN): 9781681737973 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 9979.00 р. Наличие на складе: Поставка под заказ.
Описание: Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental.
The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.
Автор: Richards Dana, Hamburger Henry Название: Logic And Language Models For Computer Science (Third Edition) ISBN: 9813229209 ISBN-13(EAN): 9789813229204 Издательство: World Scientific Publishing Рейтинг: Цена: 13622.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This text presents the formal concepts underlying Computer Science.
It starts with a wide introduction to Logic with an emphasis on reasoning and proof, with chapters on Program Verification and Prolog.
The treatment of computability with Automata and Formal Languages stands out in several ways:
it emphasizes the algorithmic nature of the proofs and the reliance on simulations;
it stresses the centrality of nondeterminism in generative models and the relationship to deterministic recognition models
The style is appropriate for both undergraduate and graduate classes.
Автор: Sumathi S, Janani M Название: Neural Networks for Natural Language Processing ISBN: 1799811603 ISBN-13(EAN): 9781799811602 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 26334.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
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