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Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation
This book provides a corpus-led analysis of multi-word units (MWUs)
in English, specifically fixed pairs of nouns which are linked by a
conjunction, such as 'mum and dad', 'bride and groom' and 'law and
order'. Crucially, the occurrence pattern of such pairs is
dependent on genre, and this book aims to document the structural
distribution of some key Linked Noun Groups (LNGs). The author
looks at the usage patterns found in a range of poetry and fiction
dating from the 17th to 20th century, and also highlights the
important role such binomials play in academic English, while
acknowledging that they are far less common in casual spoken
English. His findings will be highly relevant to students and
scholars working in language teaching, stylistics, and language
technology (including AI).
This book provides readers with a practical guide to the principles
of hybrid approaches to natural language processing (NLP) involving
a combination of neural methods and knowledge graphs. To this end,
it first introduces the main building blocks and then describes how
they can be integrated to support the effective implementation of
real-world NLP applications. To illustrate the ideas described, the
book also includes a comprehensive set of experiments and exercises
involving different algorithms over a selection of domains and
corpora in various NLP tasks. Throughout, the authors show how to
leverage complementary representations stemming from the analysis
of unstructured text corpora as well as the entities and relations
described explicitly in a knowledge graph, how to integrate such
representations, and how to use the resulting features to
effectively solve NLP tasks in a range of domains. In addition, the
book offers access to executable code with examples, exercises and
real-world applications in key domains, like disinformation
analysis and machine reading comprehension of scientific
literature. All the examples and exercises proposed in the book are
available as executable Jupyter notebooks in a GitHub repository.
They are all ready to be run on Google Colaboratory or, if
preferred, in a local environment. A valuable resource for anyone
interested in the interplay between neural and knowledge-based
approaches to NLP, this book is a useful guide for readers with a
background in structured knowledge representations as well as those
whose main approach to AI is fundamentally based on logic. Further,
it will appeal to those whose main background is in the areas of
machine and deep learning who are looking for ways to leverage
structured knowledge bases to optimize results along the NLP
downstream.
This book constitutes the refereed proceedings of the 16th
International Conference on Integrated Formal Methods, IFM 2019,
held in Lugano, Switzerland, in November 2020. The 24 full papers
and 2 short papers were carefully reviewed and selected from 63
submissions. The papers cover a broad spectrum of topics:
Integrating Machine Learning and Formal Modelling; Modelling and
Verification in B and Event-B; Program Analysis and Testing;
Verification of Interactive Behaviour; Formal Verification; Static
Analysis; Domain-Specific Approaches; and Algebraic Techniques.
Build end-to-end industrial-strength NLP models using advanced
morphological and syntactic features in spaCy to create real-world
applications with ease Key Features Gain an overview of what spaCy
offers for natural language processing Learn details of spaCy's
features and how to use them effectively Work through practical
recipes using spaCy Book DescriptionspaCy is an industrial-grade,
efficient NLP Python library. It offers various pre-trained models
and ready-to-use features. Mastering spaCy provides you with
end-to-end coverage of spaCy's features and real-world
applications. You'll begin by installing spaCy and downloading
models, before progressing to spaCy's features and prototyping
real-world NLP apps. Next, you'll get familiar with visualizing
with spaCy's popular visualizer displaCy. The book also equips you
with practical illustrations for pattern matching and helps you
advance into the world of semantics with word vectors. Statistical
information extraction methods are also explained in detail. Later,
you'll cover an interactive business case study that shows you how
to combine all spaCy features for creating a real-world NLP
pipeline. You'll implement ML models such as sentiment analysis,
intent recognition, and context resolution. The book further
focuses on classification with popular frameworks such as
TensorFlow's Keras API together with spaCy. You'll cover popular
topics, including intent classification and sentiment analysis, and
use them on popular datasets and interpret the classification
results. By the end of this book, you'll be able to confidently use
spaCy, including its linguistic features, word vectors, and
classifiers, to create your own NLP apps. What you will learn
Install spaCy, get started easily, and write your first Python
script Understand core linguistic operations of spaCy Discover how
to combine rule-based components with spaCy statistical models
Become well-versed with named entity and keyword extraction Build
your own ML pipelines using spaCy Apply all the knowledge you've
gained to design a chatbot using spaCy Who this book is forThis
book is for data scientists and machine learners who want to excel
in NLP as well as NLP developers who want to master spaCy and build
applications with it. Language and speech professionals who want to
get hands-on with Python and spaCy and software developers who want
to quickly prototype applications with spaCy will also find this
book helpful. Beginner-level knowledge of the Python programming
language is required to get the most out of this book. A
beginner-level understanding of linguistics such as parsing, POS
tags, and semantic similarity will also be useful.
This book provides a new multi-method, process-oriented approach
towards speech quality assessment, which allows readers to examine
the influence of speech transmission quality on a variety of
perceptual and cognitive processes in human listeners. Fundamental
concepts and methodologies surrounding the topic of
process-oriented quality assessment are introduced and discussed.
The book further describes a functional process model of human
quality perception, which theoretically integrates results obtained
in three experimental studies. This book's conceptual ideas,
empirical findings, and theoretical interpretations should be of
particular interest to researchers working in the fields of Quality
and Usability Engineering, Audio Engineering, Psychoacoustics,
Audiology, and Psychophysiology.
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 Apply deep learning
algorithms and techniques such as BiLSTMS, CRFs, BPE and more using
TensorFlow 2 Explore applications like text generation,
summarization, weakly supervised labelling and more Read cutting
edge material with seminal papers provided in the GitHub repository
with full working code Book DescriptionRecently, there have been
tremendous advances in NLP, and we are now moving from research
labs into practical applications. This book comes with a perfect
blend of both the theoretical and practical aspects of trending and
complex NLP techniques. The book is focused on innovative
applications in the field of NLP, language generation, and dialogue
systems. It helps you apply the concepts of pre-processing text
using techniques such as tokenization, parts of speech tagging, and
lemmatization using popular libraries such as Stanford NLP and
SpaCy. You will build Named Entity Recognition (NER) from scratch
using Conditional Random Fields and Viterbi Decoding on top of
RNNs. 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 chatbots. You will learn how to apply transfer
learning and fine-tuning using TensorFlow 2. Further, it covers
practical techniques that can simplify the labelling of textual
data. The book also has a working code that is adaptable to your
use cases for each tech piece. By the end of the 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 Use transfer and weakly supervised learning using
libraries like Snorkel Do sentiment analysis using BERT Apply
encoder-decoder NN architectures and beam search for summarizing
texts Use Transformer models with attention to bring images and
text together Build apps that generate captions and answer
questions about images using custom Transformers Use advanced
TensorFlow techniques like learning rate annealing, custom layers,
and custom loss functions to build the latest DeepNLP models Who
this book is forThis 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 and professionals who already use
TensorFlow/Python for purposes such as data science, ML, research,
analysis, etc.
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 Features Build 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
models Go through hands-on applications in Python using Google
Colaboratory Notebooks with nothing to install on a local machine
Test transformer models on advanced use cases Book DescriptionThe
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 learn Use the latest pretrained transformer models Grasp the
workings of the original Transformer, GPT-2, BERT, T5, and other
transformer models Create language understanding Python programs
using concepts that outperform classical deep learning models Use a
variety of NLP platforms, including Hugging Face, Trax, and
AllenNLP Apply Python, TensorFlow, and Keras programs to sentiment
analysis, text summarization, speech recognition, machine
translations, and more Measure the productivity of key transformers
to define their scope, potential, and limits in production Who this
book is forSince 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.
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.
We intend to edit a Festschrift for Henk Moed combining a "best of"
collection of his papers and new contributions (original research
papers) by authors having worked and collaborated with him. The
outcome of this original combination aims to provide an overview of
the advancement of the field in the intersection of bibliometrics,
informetrics, science studies and research assessment.
Discover how to integrate KNIME Analytics Platform with deep
learning libraries to implement artificial intelligence solutions
Key Features Become well-versed with KNIME Analytics Platform to
perform codeless deep learning Design and build deep learning
workflows quickly and more easily using the KNIME GUI Discover
different deployment options without using a single line of code
with KNIME Analytics Platform Book DescriptionKNIME Analytics
Platform is an open source software used to create and design data
science workflows. This book is a comprehensive guide to the KNIME
GUI and KNIME deep learning integration, helping you build neural
network models without writing any code. It'll guide you in
building simple and complex neural networks through practical and
creative solutions for solving real-world data problems. Starting
with an introduction to KNIME Analytics Platform, you'll get an
overview of simple feed-forward networks for solving simple
classification problems on relatively small datasets. You'll then
move on to build, train, test, and deploy more complex networks,
such as autoencoders, recurrent neural networks (RNNs), long
short-term memory (LSTM), and convolutional neural networks (CNNs).
In each chapter, depending on the network and use case, you'll
learn how to prepare data, encode incoming data, and apply best
practices. By the end of this book, you'll have learned how to
design a variety of different neural architectures and will be able
to train, test, and deploy the final network. What you will learn
Use various common nodes to transform your data into the right
structure suitable for training a neural network Understand neural
network techniques such as loss functions, backpropagation, and
hyperparameters Prepare and encode data appropriately to feed it
into the network Build and train a classic feedforward network
Develop and optimize an autoencoder network for outlier detection
Implement deep learning networks such as CNNs, RNNs, and LSTM with
the help of practical examples Deploy a trained deep learning
network on real-world data Who this book is forThis book is for
data analysts, data scientists, and deep learning developers who
are not well-versed in Python but want to learn how to use KNIME
GUI to build, train, test, and deploy neural networks with
different architectures. The practical implementations shown in the
book do not require coding or any knowledge of dedicated scripts,
so you can easily implement your knowledge into practical
applications. No prior experience of using KNIME is required to get
started with this book.
This book focuses on dialog from a varied combination of fields:
Linguistics, Philosophy of Language and Computation. It builds on
the hypothesis that meaning in human communication arises at the
discourse level rather than at the word level. The book offers a
complex analytical framework and integration of the central areas
of research around human communication. The content revolves around
meaning but it also gives evidence of the connection among
different points of view. Besides discussing issues of general
interest to the field, the book triggers theoretical argumentation
that is currently under scientific discussion. It examines such
topics as immanent reasoning joined with Recanati's lekta and free
enrichment, challenges of internet conversation, inner dialogs,
cognition and language, and the relation between assertion and
denial. It proposes a dialogical framework for intra-negotiation
and gives a geolinguistic perspective on spoken discourse. Finally,
it examines dialog and abduction and sheds light on a generation of
dialog contexts by means of multimodal logic applied to speech
acts.
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