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Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation
Der vorliegende Samrnelband dokumentiert die GLDV-Jahrestagung 1985
"Sprachverarbei- tung in Information und Dokumentation". Die
Gesellschaft fur Linguistische Datenver- arbeitung veranstaltete
die Tagung in Kooperation mit der Fachgruppe 3 "Naturlich-
sprachliche Systeme" im FA 1.2 der Gesellschaft fur Informatik
(GI). Das programrnkomitee, besetzt mit Christopher Habel,
Hans-Dieter Lutz, Wolfgang Wahlster und den Herausgebern, die auch
die Tagung organisierten, wahlte die Beitrage unter dem
Gesichtspunkt einer aufgeschlossenen Zusamrnenarbeit von Informa-
tionswissenschaft, Kunstlicher Intelligenz und Linguistischer
Datenverarbeitung aus. Rainer Kuhlen entwickelt in seinem
Einleitungsreferat den Bezugsrahmen fur die weiteren Beitrage. Er
weist nachdrucklich auf die handlungsorientierte Zielsetzung von
Informationssystemen hin und auf die daraus resultierende Forderung
an Wissen- schaftler, bei ihren Beitragen zur Gestaltung von
Informationssystemen die pragma- tische Ausrichtung des
Gesamtsystems angemessen zu berucksichtigen. Die lebhafte
Diskussion uber die zinzelnen Vortrage wahrend der Tagung machte
deutlich, wie sehr in einem Wissenschaftsfeld generell akzeptierte
Leitvorstellungen Raum fur kontrare Meinungen und
fruchtbar-heterogene Forschungsansatze lassen (oder schaffen). Wie
verschieden die in der Forschung vertretenen Gesichtspunkte sind,
zeigt schon die thematische Grobgruppierung der Vortrage, die als
Gliederung des Tagungsbandes beibehalten wurde. Die in diesem Band
nicht festgehaltenen Diskussionsveranstaltungen uber "LDV-Aus-
bildung und-Berufsperspektiven" (Betreuung Hans-Dieter Lutz) und
"Verbundforschung" (Betreuung Tobias Bruckner und Brigitte
Endres-Niggemeyer) mit ihrem teilweise mehr als lebhaften Verlauf
trugen dem Bedarf nach fachlicher und wissenschafts- politischer
Meinungsbildung Rechnung.
Um der starken Nachfrage nach Ausbildung und Fortbildung im Bereich
der Kunstlichen Intelligenz Rechnung zu tragen, wurde vom
Fachausschuss 1.2 ''Kunstliche Intelligenz und Mustererkennung" der
Gesellschaft fur Informatik vom 5. -16. Marz 1984 in DasseI
(Solling) eine zweiwoechige Fruhjahrsschule durchgefuhrt. Diese
Fruhjahrsschule war die Nachfolgeveranstaltung zur KIFS-82, die im
Marz 1982 in Teisendorf stattfand. Die diesjahrige KIFS stand unter
dem Themenschwerpunkt "Reprasentation von Wissen und
naturlichsprachliche Systeme". Das Kursangebot umfasste:
Gruldkurse: - Bildverstehen (B. Neumann, Hamburg) - Automatisches
Beweisen (J. Siekmann, Kaiserslautern) - Naturlichsprachliche
Systeme (W. Wahlster, Saarbrucken) Aufbaukurse: - Inferenzmethoden
(W. Bibel, Munchen) - Parser als integraler Bestandteil von
Sprachverarbeitungssystemen (T. Christaller, Hamburg) - Lernen und
Wissensakquisition (Ch. Habel & C.-R. Rollinger, Berlin) -
Techniken der Wissensdarstellung (J. Laubseh, Stuttgart) -
Textverstehen und Textproduktion (U. Quasthoff-Hartmann,
Bielefeld). - Semantik odelle in der Kunstlichen Intelligenz (C.
Schwind, Marseille) Spezialkurse: - LISP (G. Goerz, Erlangen) -
LISP 2 (H. Stoyan, Erlangen) - PROLOG (H. Gust & M. Koenig,
Osnabruck/ Berlin) Die Durchfuhrung der Spezialkurse, die eine
Einfuhrung bzw. Vertiefung der fur die KI wichtigsten
Programmiersprachen zum Ziel hatten, erfolgte auf Kleinrechnern
(verschiedener Hersteller) und zum Teil auf einer LISP aschine. Die
Rechner wurden ausserdem fur UEbungen und Vorfuhrungen zu einigen
der weiteren Kurse verwendet. Hierdurch wurde es moeglich, die im
Vorlesungsteil der Kurse erworbenen theoretischen Kenntnisse,
teilweise sogar am Rechner anzuwenden.
To sustain and stay at the top of the market and give absolute
comfort to the consumers, industries are using different strategies
and technologies. Natural language processing (NLP) is a technology
widely penetrating the market, irrespective of the industry and
domains. It is extensively applied in businesses today, and it is
the buzzword in every engineer's life. NLP can be implemented in
all those areas where artificial intelligence is applicable either
by simplifying the communication process or by refining and
analyzing information. Neural machine translation has improved the
imitation of professional translations over the years. When applied
in neural machine translation, NLP helps educate neural machine
networks. This can be used by industries to translate low-impact
content including emails, regulatory texts, etc. Such machine
translation tools speed up communication with partners while
enriching other business interactions. Deep Natural Language
Processing and AI Applications for Industry 5.0 provides innovative
research on the latest findings, ideas, and applications in fields
of interest that fall under the scope of NLP including
computational linguistics, deep NLP, web analysis, sentiments
analysis for business, and industry perspective. This book covers a
wide range of topics such as deep learning, deepfakes, text mining,
blockchain technology, and more, making it a crucial text for
anyone interested in NLP and artificial intelligence, including
academicians, researchers, professionals, industry experts,
business analysts, data scientists, data analysts, healthcare
system designers, intelligent system designers, practitioners, and
students.
Work through interesting real-life business use cases to uncover
valuable insights from unstructured text using AWS AI services Key
Features Get to grips with AWS AI services for NLP and find out how
to use them to gain strategic insights Run Python code to use
Amazon Textract and Amazon Comprehend to accelerate business
outcomes Understand how you can integrate human-in-the-loop for
custom NLP use cases with Amazon A2I Book DescriptionNatural
language processing (NLP) uses machine learning to extract
information from unstructured data. This book will help you to move
quickly from business questions to high-performance models in
production. To start with, you'll understand the importance of NLP
in today's business applications and learn the features of Amazon
Comprehend and Amazon Textract to build NLP models using Python and
Jupyter Notebooks. The book then shows you how to integrate AI in
applications for accelerating business outcomes with just a few
lines of code. Throughout the book, you'll cover use cases such as
smart text search, setting up compliance and controls when
processing confidential documents, real-time text analytics, and
much more to understand various NLP scenarios. You'll deploy and
monitor scalable NLP models in production for real-time and batch
requirements. As you advance, you'll explore strategies for
including humans in the loop for different purposes in a document
processing workflow. Moreover, you'll learn best practices for
auto-scaling your NLP inference for enterprise traffic. Whether
you're new to ML or an experienced practitioner, by the end of this
NLP book, you'll have the confidence to use AWS AI services to
build powerful NLP applications. What you will learn Automate
various NLP workflows on AWS to accelerate business outcomes Use
Amazon Textract for text, tables, and handwriting recognition from
images and PDF files Gain insights from unstructured text in the
form of sentiment analysis, topic modeling, and more using Amazon
Comprehend Set up end-to-end document processing pipelines to
understand the role of humans in the loop Develop NLP-based
intelligent search solutions with just a few lines of code Create
both real-time and batch document processing pipelines using Python
Who this book is forIf you're an NLP developer or data scientist
looking to get started with AWS AI services to implement various
NLP scenarios quickly, this book is for you. It will show you how
easy it is to integrate AI in applications with just a few lines of
code. A basic understanding of machine learning (ML) concepts is
necessary to understand the concepts covered. Experience with
Jupyter notebooks and Python will be helpful.
Take a problem-solving approach to learning all about transformers
and get up and running in no time by implementing methodologies
that will build the future of NLP Key Features Explore quick
prototyping with up-to-date Python libraries to create effective
solutions to industrial problems Solve advanced NLP problems such
as named-entity recognition, information extraction, language
generation, and conversational AI Monitor your model's performance
with the help of BertViz, exBERT, and TensorBoard Book
DescriptionTransformer-based language models have dominated natural
language processing (NLP) studies and have now become a new
paradigm. With this book, you'll learn how to build various
transformer-based NLP applications using the Python Transformers
library. The book gives you an introduction to Transformers by
showing you how to write your first hello-world program. You'll
then learn how a tokenizer works and how to train your own
tokenizer. As you advance, you'll explore the architecture of
autoencoding models, such as BERT, and autoregressive models, such
as GPT. You'll see how to train and fine-tune models for a variety
of natural language understanding (NLU) and natural language
generation (NLG) problems, including text classification, token
classification, and text representation. This book also helps you
to learn efficient models for challenging problems, such as
long-context NLP tasks with limited computational capacity. You'll
also work with multilingual and cross-lingual problems, optimize
models by monitoring their performance, and discover how to
deconstruct these models for interpretability and explainability.
Finally, you'll be able to deploy your transformer models in a
production environment. By the end of this NLP book, you'll have
learned how to use Transformers to solve advanced NLP problems
using advanced models. What you will learn Explore state-of-the-art
NLP solutions with the Transformers library Train a language model
in any language with any transformer architecture Fine-tune a
pre-trained language model to perform several downstream tasks
Select the right framework for the training, evaluation, and
production of an end-to-end solution Get hands-on experience in
using TensorBoard and Weights & Biases Visualize the internal
representation of transformer models for interpretability Who this
book is forThis book is for deep learning researchers, hands-on NLP
practitioners, as well as ML/NLP educators and students who want to
start their journey with Transformers. Beginner-level machine
learning knowledge and a good command of Python will help you get
the best out of this book.
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.
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.
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.
Become a proficient NLP data scientist by developing deep learning
models for NLP and extract valuable insights from structured and
unstructured data Key Features Get to grips with word embeddings,
semantics, labeling, and high-level word representations using
practical examples Learn modern approaches to NLP and explore
state-of-the-art NLP models using PyTorch Improve your NLP
applications with innovative neural networks such as RNNs, LSTMs,
and CNNs Book DescriptionIn the internet age, where an increasing
volume of text data is generated daily from social media and other
platforms, being able to make sense of that data is a crucial
skill. With this book, you'll learn how to extract valuable
insights from text by building deep learning models for natural
language processing (NLP) tasks. Starting by understanding how to
install PyTorch and using CUDA to accelerate the processing speed,
you'll explore how the NLP architecture works with the help of
practical examples. This PyTorch NLP book will guide you through
core concepts such as word embeddings, CBOW, and tokenization in
PyTorch. You'll then learn techniques for processing textual data
and see how deep learning can be used for NLP tasks. The book
demonstrates how to implement deep learning and neural network
architectures to build models that will allow you to classify and
translate text and perform sentiment analysis. Finally, you'll
learn how to build advanced NLP models, such as conversational
chatbots. By the end of this book, you'll not only have understood
the different NLP problems that can be solved using deep learning
with PyTorch, but also be able to build models to solve them. What
you will learn Use NLP techniques for understanding, processing,
and generating text Understand PyTorch, its applications and how it
can be used to build deep linguistic models Explore the wide
variety of deep learning architectures for NLP Develop the skills
you need to process and represent both structured and unstructured
NLP data Become well-versed with state-of-the-art technologies and
exciting new developments in the NLP domain Create chatbots using
attention-based neural networks Who this book is forThis PyTorch
book is for NLP developers, machine learning and deep learning
developers, and anyone interested in building intelligent language
applications using both traditional NLP approaches and deep
learning architectures. If you're looking to adopt modern NLP
techniques and models for your development projects, this book is
for you. Working knowledge of Python programming, along with basic
working knowledge of NLP tasks, is required.
Get well-versed with traditional as well as modern natural language
processing concepts and techniques Key Features Perform various NLP
tasks to build linguistic applications using Python libraries
Understand, analyze, and generate text to provide accurate results
Interpret human language using various NLP concepts, methodologies,
and tools Book DescriptionNatural Language Processing (NLP) is the
subfield in computational linguistics that enables computers to
understand, process, and analyze text. This book caters to the
unmet demand for hands-on training of NLP concepts and provides
exposure to real-world applications along with a solid theoretical
grounding. This book starts by introducing you to the field of NLP
and its applications, along with the modern Python libraries that
you'll use to build your NLP-powered apps. With the help of
practical examples, you'll learn how to build reasonably
sophisticated NLP applications, and cover various methodologies and
challenges in deploying NLP applications in the real world. You'll
cover key NLP tasks such as text classification, semantic
embedding, sentiment analysis, machine translation, and developing
a chatbot using machine learning and deep learning techniques. The
book will also help you discover how machine learning techniques
play a vital role in making your linguistic apps smart. Every
chapter is accompanied by examples of real-world applications to
help you build impressive NLP applications of your own. By the end
of this NLP book, you'll be able to work with language data, use
machine learning to identify patterns in text, and get acquainted
with the advancements in NLP. What you will learn Understand how
NLP powers modern applications Explore key NLP techniques to build
your natural language vocabulary Transform text data into
mathematical data structures and learn how to improve text mining
models Discover how various neural network architectures work with
natural language data Get the hang of building sophisticated text
processing models using machine learning and deep learning Check
out state-of-the-art architectures that have revolutionized
research in the NLP domain Who this book is forThis NLP Python book
is for anyone looking to learn NLP's theoretical and practical
aspects alike. It starts with the basics and gradually covers
advanced concepts to make it easy to follow for readers with
varying levels of NLP proficiency. This comprehensive guide will
help you develop a thorough understanding of the NLP methodologies
for building linguistic applications; however, working knowledge of
Python programming language and high school level mathematics is
expected.
Noch niemals ist die Zusammenarbeit der deutschsprachigen mit der
angelsachsischen Welt in technischen Fragen so eindringlich und
umfassend gewesen wie in den Jahren seit dem Ende des zweiten
Weltkrieges. Das unter dem Namen des MARSHALL-Plans bekannte
Aufbauwerk des amerikanischen Volkes hat in seiner Durchfuhrung
einen besonders verstarkten Schriftverkehr uber technische
Einzelheiten mit der Notwendigkeit der deutsch-englischen
UEbersetzung mit sich gebracht; das Berg-und Huttenwesen steht
dabei mit in der vordersten Reihe der Gebiete, fur die eine solche
Aufgabe erwachsen ist. Auch ist es heute fur den Ingenieur in den
Planungsstellen, in den Betrieben, in den Statten der
wissenschaftlichen Forschung oder im Patentwesen, aber ebenso
bereits fur den Studenten der technischen Facher mehr denn je zur
zwingenden Forderung geworden, das englisch geschriebene
Fachschrifttum verfolgen zu koennen. Technisches Englisch ist nun
bekanntlich eine Sprache, die in vielen Belangen uber einen anderen
Wortschatz und eine andere Zuordnung von Begriff und Wort verfugt
als das Englisch des sonstigen taglichen Lebens oder des
schoengeistigen Schrifttums. Bedenkliche Missverstand- nisse
koennen entstehen, wenn dieser Tatsache nicht Rechnung getragen
wird. UEberdies ist die technische Sprache schnellehig wandelbar
wie die Technik selbst; die Schwierigkeiten, die daraus entstehen,
treten dem Benutzer der bisher erschienenen technischen
Fachwoerterbucher immer wieder einmal entgegen.
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.
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