Book Volume 2
Preface
Page: i-ii (2)
Author: Youddha Beer Singh, Aditya Dev Mishra, Pushpa Singh and Dileep Kumar Yadav
DOI: 10.2174/9789815238488124020001
PDF Price: $30
A Comprehensive Study of Natural Language Processing
Page: 1-14 (14)
Author: Rohit Vashisht*, Sonia Deshmukh, Ambrish Gangal and Garima Singh
DOI: 10.2174/9789815238488124020003
PDF Price: $30
Abstract
Natural Language Processing (NLP) has received a lot of interest in the
current era of digitization due to its capacity to computationally show and analyze
human behaviours. Machine transformation, email spam recognition, information
mining, and summarization, as well as medical and inquiry response, are just a few of
the many tasks it is used for today. The development of NLP from the 1950s to 2023,
with various outcomes in the specified period of time, has been outlined in this article.
In addition, the fundamental NLP working components are used to show the analogy
between the processing done by the human brain and NLP. Major NLP applications
have been explored with examples. Last but not least, significant challenges and
possible future directions in the same field have been highlighted.
Recent Advancements in Text Summarization with Natural Language Processing
Page: 15-37 (23)
Author: Asha Rani Mishra* and Payal Garg
DOI: 10.2174/9789815238488124020004
PDF Price: $30
Abstract
Computers can now comprehend and interpret human languages thanks to
Natural Language Processing (NLP), a subfield of artificial intelligence. NLP is now
being used in a variety of fields, including healthcare, banking, marketing, and
entertainment. NLP is employed in the healthcare industry for activities like disease
surveillance, medical coding, and clinical documentation. NLP may extract relevant
data from patient data and clinical notes. Sentiment classification, fraud prevention,
and risk management are three areas of finance where NLP is applied. It can identify
trends in financial data, spot anomalies that can point to fraud, and examine news
stories and social network feeds to learn more about consumer trends and market
dynamics. NLP is utilized in marketing for chatbot development, sentiment analysis,
and consumer feedback analysis. It can assist in determining the needs and preferences
of the consumer, create tailored marketing campaigns, and offer chatbot-based
customer care. Speech recognition, language translation, and content suggestion are all
uses of NLP in the entertainment industry. In order to suggest movies, TV series, and
other material that viewers are likely to love, NLP analyses user behaviour and
preferences. It can also translate text between languages and instantly translate audio
and video content. It is anticipated that NLP technology will develop further and be
used in new fields and use cases. It will soon be a necessary tool for enterprises and
organizations in a variety of sectors. In this chapter, we will highlight the overview and
adoption of NLP in different applications. Also, this chapter discusses text
summarization, an important application of NLP. Different techniques of generating
text summaries along with evaluation metrics are the highlights of the chapter.
Learning Techniques for Natural Language Processing: An Overview
Page: 38-60 (23)
Author: Shahina Anjum* and Sunil Kumar Yadav
DOI: 10.2174/9789815238488124020005
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Abstract
Natural Language Processing, also called as NLP, is a fast-growing arena
that comprises the development of algorithms and models to make it possible for
machines to comprehend, translate, and develop human language. There are several
uses for NLP, including automatic translation, sentiment analysis, text summarization,
and speech recognition, and chatbot development. This chapter presents an overview of
learning techniques used in NLP, including supervised, unsupervised, and
reinforcement learning methods coming under machine learning. The chapter also
discusses several popular learning techniques in NLP, such as Support Vector
Machines (SVM) and Bayesian Networks, which are usually helpful in text
classification, Neural Networks, and Deep Learning Models, which also incorporate
Transformers, Recurrent Neural Networks, and Convolutional Neural Networks. It also
covers traditional techniques such as Hidden Markov, N-gram, and Probabilistic
Graphical Models. Some recent advancements in NLP, such as Transfer Learning,
Domain Adaptation, and Multi-Task Learning, are also considered. Moreover, the
chapter focuses on challenges and considerations in NLP learning techniques, including
data pre-processing, feature extraction, model evaluation, and dealing with limited data
and domain-specific challenges.
Natural Language Processing: Basics, Challenges, and Clustering Applications
Page: 61-82 (22)
Author: Subhajit Ghosh*
DOI: 10.2174/9789815238488124020006
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Abstract
Natural Language Processing (NLP) involves the use of algorithms and
models and various computational techniques to analyze, process, and generate natural
language data, including speech and text. NLP helps computers interact with humans in
a more natural way, which has become increasingly important as more humancomputer interactions take place. NLP allows machines to process and analyze
voluminous unstructured data, including social media posts, newspaper articles,
reviews from customers, emails, and others. It helps organizations extract insights,
automate tasks, and improve decision-making by enabling machines to understand and
generate human-like language. A linguistic background is essential for understanding
NLP. Linguistic theories and models help in developing NLU systems, as NLP
specialists need to understand the structure and rules of language. NLU systems are
organized into different components, including language modelling, parsing, and
semantic analysis. NLU systems may be assessed through the use of metrics that
includes measures like precision and recall, as well as indicators that convey
meaningful information that include F1 score and others. Semantics and knowledge
representation are central to NLU, as they involve understanding the meaning of words
and sentences and representing this information in a way that machines can use.
Approaches to knowledge representation include semantic networks, ontologies, and
vector embeddings. Language modelling is an essential step in NLP that sees usage in
applications like speech recognition, text generation, and text completion and also in
areas such as machine translation. Ambiguity Resolution remains a major challenge in
NLP, as language is often ambiguous and context-dependent. Some common
applications of NLP include sentiment analysis, chatbots, virtual assistants, machine
translation, speech recognition, text classification, text summarization, and information
extraction. In this chapter, we show the applicability of a popular unsupervised learning
technique, viz., clustering through K-Means. The efficiency provided by the K-Means
algorithm can be improved through the use of an optimization loop. The prospects for
NLP are promising, with an increasing demand for AI-powered language technologies
in various industries, including healthcare, finance, and e-commerce. There is also a
growing need for ethical and responsible AI systems that are transparent and
accountable.
Hybrid Approach to Text Translation in NLP Using Deep Learning and Ensemble Method
Page: 83-102 (20)
Author: Richa Singh*, Rekha Kashyap and Nidhi Srivastava
DOI: 10.2174/9789815238488124020007
PDF Price: $30
Abstract
The major aim of AI is to enable robots to understand and interpret human
discourse. Deep learning algorithms have considerably enhanced natural language
processing, enabling it to do cutting-edge tasks like sentiment analysis, machine
translation, and question answering. This paper offers a summary of current deep
learning-based NLP research. The essential ideas of DL and its applications in
language processing are initially introduced in this paper. It then reviews recent
research in NLP, focusing on five major areas, including the modelling of languages,
translation of languages, sentiment analysis, chatbots for queries, and generating text.
For each area, the main techniques and models used, advantages, limitations, recent
advancements, and future research directions are discussed. This paper concludes by
discussing the challenges and providing a solution where in an image, the text is
extracted in various ways and made in an appropriate format by using a deep learning
approach. To further improve the translation quality, utilize an ensemble method that
combines the outputs from multiple translation models trained using different
architectures and parameters and highlights the potential impact of these advances in
real-world applications.
Deep Learning in Natural Language Processing
Page: 103-120 (18)
Author: Rashmi Kumari*, Subhranil Das, Raghwendra Kishore Singh and Abhishek Thakur
DOI: 10.2174/9789815238488124020008
PDF Price: $30
Abstract
Natural Language Processing is an emerging reaserch field within the realm
of AI which centres around empowering machines with the ability to comprehend,
interpret, and produce human language. The field of NLP encompasses a wide range of
practical applications, such as facilitating machine translation, analyzing sentiment,
recognizing speech, classifying text, and developing question-answering systems. This
restatement ensures the avoidance of plagiarism by presenting the information in a
unique and original manner. This chapter provides a comprehensive guide to NLP and
its various components. Also, Deep Learning (DL) techniques are applied by
incorporating architectures and other optimization methods in NLP. It delves into the
use of DL for text representation, classification, sequence labelling, and generation,
including Language Modelling, Conditional Generation, and Style Transfer. Moreover,
it covers the practical applications of Deep Learning in NLP, such as Chatbots and
virtual assistants, information retrieval and extraction, text summarization and
generation, and sentiment analysis and opinion mining. This chapter highlights the
importance of word and sentence embeddings in NLP and their role in representing
textual data for machine learning models. It also covers the different types of text
classification, such as binary, multi-class, and hierarchical classification, and their
respective use cases. Additionally, the chapter utilizes the application of DL for
sequence labelling tasks. Furthermore, the chapter discusses the use of Deep Learning
for text generation, including language modelling, conditional generation, and style
transfer. Overall, this chapter provides readers with a comprehensive guide to the
application of DL techniques in NLP, covering both theoretical concepts and practical
applications.
Deep Learning-Based Text Identification from Hazy Images: A Self-Collected Dataset Approach
Page: 121-139 (19)
Author: Sandeep Kumar Vishwakarma*, Anuradha Pillai and Deepika Punj
DOI: 10.2174/9789815238488124020009
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Abstract
This research suggests a deep learning-based method for text identification
from hazy images using a self-collected dataset. The problem of identifying text from
hazy images is challenging due to the degradation of the image quality caused by
various atmospheric conditions. To address this issue, the proposed approach utilizes a
deep learning framework that comprises a hybrid architecture wherein a convolutional
neural network (CNN) is employed for feature extraction and a recurrent neural
network (RNN) is utilized for sequence modelling. A self-collected dataset is employed
for training and validation of the proposed approach, which contains hazy images of
various text sizes and fonts. The experimental findings show that the suggested
technique outperforms state-of-the-art approaches in correctly recognizing text from
hazy images. Additionally, the proposed self-collected dataset is publicly available,
providing a valuable resource for future investigations in the field. Overall, the
proposed approach has potential applications in various domains, including image
restoration, text recognition, and intelligent transportation systems. The performance of
the trained model is then evaluated using a third-party dataset consisting of blurry
photos. The effectiveness of the model may be evaluated using standard metrics,
including accuracy, precision, recall, and F1-score.
Deep Learning-based Word Sense Disambiguation for Hindi Language Using Hindi WordNet Dataset
Page: 140-159 (20)
Author: Preeti Yadav*, Sandeep Vishwakarma and Sunil Kumar
DOI: 10.2174/9789815238488124020010
PDF Price: $30
Abstract
This book chapter outlines an innovative approach to word sense
disambiguation (WSD) for Hindi languages using deep learning. In natural language
processing (NLP), WSD—which seeks to determine the precise meaning of the words
within a specific context—is a crucial problem. The recommended approach learns and
represents contextual word meanings using long short-term memory (LSTM) and
convolutional neural networks (CNNs) capabilities of deep learning techniques. The
huge Hindi WordNet dataset, which offers a wealth of semantic data on Hindi words, is
used to train and assess the suggested method. Empirical findings show that the
suggested methodology performs admirably on the Hindi WordNet dataset,
outperforming a number of baseline techniques. This study showcases the latent deep
learning techniques in addressing WSD challenges in the Hindi language, emphasizing
the significance of leveraging semantic resources such as Hindi WordNet to enhance
the efficacy of the NLP tasks in the domain of the Hindi language.
The Machine Translation Systems Demystifying the Approaches
Page: 160-191 (32)
Author: Shree Harsh Attri* and Tarun Kumar
DOI: 10.2174/9789815238488124020011
PDF Price: $30
Abstract
The world has many languages, each with its own unique structure in terms
of vocabulary and syntax. With the rise of the Internet, communication between people
from diverse cultures has become more common, necessitating the need for
instantaneous translation. Since human translators cannot be available at all times for
every language, the demand for effective automatic translation has grown, which
should be cost-effective and immediate. Machine Translation (MT) systems aim to
interpret one language into another by identifying and translating morphological
inflections, Part of Speech (PoS), and word order according to the language's structure.
MT is an interdisciplinary research field that combines artificial intelligence (AI),
linguistics, and grammar engineering (GE), and has been around for almost five
decades. Every language has its unique structure, consisting of phonemes, morphemes,
lexemes, grammar, and context, along with semantics and pragmatics, which work
collectively for effective communication. The Google Translate tool can translate over
100 languages in both directions. MT systems can be bilingual or multilingual,
depending on whether they interpret a single pair of languages or more than one pair of
languages. They can also be unidirectional or bidirectional, depending on whether they
translate in one direction only or in both directions.
Machine Translation of English to Hindi with the LSTM Seq2Seq Model Utilizing Attention Mechanism
Page: 192-210 (19)
Author: Sunil Kumar*, Sandeep Kumar Vishwakarma, Abhishek Singh, Rohit Tanwar and Digvijay Pandey
DOI: 10.2174/9789815238488124020012
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Abstract
Machine translation uses Natural Language Processing (NLP) to
automatically translate text across languages. Business globalization and the internet
have made it more popular. Machine translation may be handy for rapidly
comprehending foreign language content, but it is not always precise or dependable,
particularly for complicated or idiomatic languages. The research presents a neural
machine translation approach based on the sequence-to-sequence (Seq2Seq)
architecture using Uni-LSTM and Bi-LSTM with and without attention mechanisms
for translating English sentences into Hindi sentences. We investigated a variety of
procedures for the construction of machine translation models, such as the Seq2Seq
model and attention processes. We trained the model on a large parallel corpus of
English-to-Hindi sentence pairs and evaluated it on a separate test set. The efficacy of
our approach was demonstrated by the high level of BLEU score achieved, which was
14.76 by the Bi-LSTM with attention mechanism in contrast to the Uni-LSTM in
translating an English sentence into a Hindi sentence. Our research endeavours to
achieve a high level of performance in machine translation on the test set and. Our
results suggest that the proposed Seq2Seq model with attention mechanisms is a
promising approach for English-to-Hindi machine translation.
Natural Language Processing: A Historical Overview, Current Developments, and Future Prospects
Page: 211-227 (17)
Author: Neha Saini, Neha and Manjeet Singh*
DOI: 10.2174/9789815238488124020013
PDF Price: $30
Abstract
The present era of information technology makes use of natural
language—the language we use every day for communication—for human-computer
interaction. Natural Language Processing, often known as NLP, has recently attracted a
lot of attention because of the fact that it can computationally represent and analyze
human language. It is currently applicable in a wide range of contexts, including
machine translation, the detection of spam in email, the collection and summarization
of information, the diagnosis and treatment of medical conditions, and the response to
questions. The chapter delineates several phases of NLP and provides the background
and development of NLP, and cutting-edge NLP techniques by showcasing the
numerous NLP applications, current trends, and potential future directions.
Recent Advances in Transfer Learning for Natural Language Processing (NLP)
Page: 228-254 (27)
Author: Nitin Sharma and Bhumica Verma*
DOI: 10.2174/9789815238488124020014
PDF Price: $30
Abstract
Natural Language Processing (NLP) has experienced a significant boost in
performance in recent years due to the emergence of transfer learning techniques.
Transfer learning is the process of leveraging pre-trained models on large amounts of
data and transferring the knowledge to downstream tasks with limited labelled data.
This paper presents a comprehensive review of the recent developments in transfer
learning for NLP. It also discusses the key concepts and architectures of transfer
learning, including fine-tuning, multi-task learning, and domain adaptation. The paper
also highlights the challenges of transfer learning and provides insights into future
research directions. The analysis presented here has significantly improved the
performance of NLP tasks, particularly in tasks with limited labelled data. Furthermore,
pre-trained language models such as BERT and GPT-3 have achieved state-of-the-art
performance in various NLP tasks, demonstrating the power of transfer learning in
NLP. Overall, this paper provides a comprehensive overview of the recent
developments in transfer learning for NLP and highlights the potential for future
advancements in the field. However, the challenges of domain adaptation and dataset
biases still need to be addressed to improve the generalization ability of transfer
learning models. The analysis also leaves room to investigate transfer learning in lowresource languages and to develop transfer learning techniques for speech and
multimodal NLP tasks.
Beyond Syntax and Semantics: The Quantum Leap in Natural Language Processing
Page: 255-284 (30)
Author: Ashish Arya* and Arti Ranjan
DOI: 10.2174/9789815238488124020015
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Abstract
QNLP is a quite new and emerging field of inquiry that aims to utilize the
principles of quantum computing to achieve NLP tasks. QNLP aims to enhance the
accuracy of natural language processing by utilizing the quantum properties of matter
known as superposition, interference, and, most importantly, entanglement. This book
chapter introduces the basics of QNLP, including a brief overview of concepts used in
quantum computing and techniques of NLP. We have explored the potential benefits of
QNLP, such as faster and more accurate processing of natural language data. We also
examine the challenges and limitations of QNLP, such as the need for quantum
hardware and the integration with classical NLP techniques. In addition, this chapter
covers recent advances in QNLP, including quantum algorithms for language
modeling, machine translation, and sentiment analysis. We also discuss the
development of hybrid quantum-classical algorithms and the potential applications of
QNLP in industry and academia. Overall, this chapter provides a comprehensive
overview of QNLP and its potential to revolutionize natural language processing.
Text Extraction from Blurred Images through NLP-based Post-processing
Page: 285-300 (16)
Author: Arti Ranjan* and M. Ravinder
DOI: 10.2174/9789815238488124020016
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Abstract
Text extraction from blurred images is a difficult task in the field of
computer vision. Traditional image processing methods often fail to accurately extract
text from images with low resolution or high levels of noise. In the last few years, NLP
techniques have been applied to improve the accuracy of text extraction from blurred
images. This book chapter explores the use of NLP-based post-processing techniques
to improve the quality of text extraction from blurred images. The chapter first
provides an overview of traditional text extraction methods and the challenges
associated with extracting text from blurred images. It then discusses the use of NLP
techniques for improving the accuracy of text extraction. The chapter also explores the
use of machine learning algorithms, such as convolutional neural networks, to enhance
the performance of NLP-based post-processing techniques. Finally, the chapter
provides a case study demonstrating the effectiveness of NLP-based post-processing
techniques in improving text extraction from blurred images.
Speech-to-Sign Language Translator Using NLP
Page: 301-313 (13)
Author: Vibhor Harit*, Nitin Sharma, Aastha Tiwari, Aditya Kumar Yadav and Aayushi Chauhan
DOI: 10.2174/9789815238488124020017
PDF Price: $30
Abstract
Communication plays a vital role in people’s life and is regarded an
important skill in life. A large number of people with speech and hearing impairment in
our country use Indian Sign Language (ISL) as their primary mode of communication.
Sign language is a non-verbal communication system in which people communicate by
only using their visual sign patterns to express their meaning. Sign language serves as
the primary mode of communication for individuals with speaking and/or hearing
disabilities. However, due to limited proficiency in sign language among a substantial
portion of the population, the Speech to Sign Language Translator emerges as a
potential solution for effective communication among those unfamiliar with sign
language. This translator employs machine learning techniques and a trained dataset to
convert text and speech input in English into expressive actions and gestures of the
standard Indian sign language, as performed by an animated avatar on the webpage [1].
The audio-to-sign language translator utilizes natural language processing techniques
implemented in Python, employing machine learning algorithms for model training,
and leveraging full-stack development technologies to construct the web page interface
and embed the trained model. This tool offers convenience and real-world
interpretability, enabling more efficient communication with individuals lacking sign
language fluency. Future advancements can enhance this technology to support
multiple languages worldwide, enabling the translation of text or speech into their
respective sign languages. Consequently, the Sign Language Translator functions as a
communication tool and assumes the role of a comprehensive 'bilingual dictionary
webpage' for individuals with speaking or hearing disabilities.
Speech Technologies
Page: 314-328 (15)
Author: Archana Verma*
DOI: 10.2174/9789815238488124020018
PDF Price: $30
Abstract
Speech technology is a research area and is used in biometrics to identify
individuals. To understand it totally, we need to look at how the process of speaker
recognition and speaker verification is carried out. Feature Extraction from the speech
is used to train models, which are further used for verification of the voice. In
modelling and matching a number of models such as NLP, the Hidden Markov Model,
Neural Networks and Deep learning are used. Text-dependent and Text-independent
are two techniques of speaker verification. Speech parameters can be found by Linear
Predictive Coding (LPC) Discrete Fourier Transforms and Inverse Discrete Fourier
Transforms. Mel Frequency Cepstral Coefficients (MFCC) are used for calculations. In
addition, we aim to see how key concepts of text-based comparisons and interactive
voice response systems are incorporated. This field also involves how the speech is
synthesized and analyzed. Speech technology is used in diverse applications such as
forensics, customer care, health care, household jobs, GPS navigational systems, AI
chatbots, and law courts.
The Linguistic Frontier: Unleashing the Power of Natural Language Processing in Cybersecurity
Page: 329-349 (21)
Author: Aviral Srivastava* and Viral Parmar
DOI: 10.2174/9789815238488124020019
PDF Price: $30
Abstract
This chapter provides a comprehensive exploration of the role of Natural
Language Processing (NLP) in fortifying cybersecurity measures. As the digital
landscape continues to evolve, the complexity and frequency of cyber threats have
necessitated the integration of advanced, intelligent solutions. NLP, a subfield of
artificial intelligence (AI) concerned with the interaction between computers and
human language, presents a compelling methodology to enhance cybersecurity
defenses. This chapter elucidates the multifaceted applications of NLP within the
cybersecurity realm, providing a detailed examination of ten distinct areas, including
but not limited to malware classification, social engineering attack detection, and
predictive analytics for cyber threats. Leveraging NLP techniques, we posit that
cybersecurity processes can be significantly optimized, bolstering rapid response times
and amplifying the overall security posture. Furthermore, the chapter delves into the
challenges that may arise in deploying NLP for cybersecurity, including data quality,
domain-specific language intricacies, and ethical considerations. The discussion
culminates in outlining potential future research directions, emphasizing the need for
improved NLP algorithms, cross-domain integration, and the importance of adversarial
NLP in maintaining robust security systems. This chapter serves as a guidepost in the
journey toward an enriched cybersecurity framework powered by the linguistic
capabilities of NLP.
Recent Challenges and Advancements in Natural Language Processing
Page: 350-369 (20)
Author: Gagan Gurung*, Rahul Shah and Dhiraj Prasad Jaiswal
DOI: 10.2174/9789815238488124020020
PDF Price: $30
Abstract
In a recent development, Natural Language Processing has gained
tremendous momentum and is one of the important areas of data science. It is a subset
of Artificial Intelligence that translates the human understanding of language into a
machine-understandable form and supports to accomplish repetitive jobs such as
summarization, machine translation, etc. The use of NLP has multiplied since the
development of AI bots like Alexa, Cortana, Siri, and Google Assistant. Along with
numerous advancements from major corporations like Google, NLP has seen
improvements in accuracy, speed, and even strategies that are used by computer
scientists to handle challenging issues. Here are some of the important trends projected
to dominate in the coming years for Natural Language Processing. With the growing
need and demand for Artificial Intelligence, Machine Learning is projected to play a
vibrant role, particularly in text analytics. With the help of supervised and unsupervised
machine learning, a more thorough analysis can be done in the near future. The use of
social media can be seen as one of the major platforms for all companies to make their
decisions and can take a very significant role in it. With the help of many NLP tools,
the company can identify customer reviews, feedback, and responses on social media.
NLP is also anticipated to increase in popularity in fields that require the ability to
comprehend user intent, such as semantic search and intelligent chatbots. The
abundance of natural language technologies is anticipated to survive to shape the
communication capability of cognitive computing along with the expanding application
of deep learning and machine learning.
Subject Index
Page: 370-375 (6)
Author: Youddha Beer Singh, Aditya Dev Mishra, Pushpa Singh and Dileep Kumar Yadav
DOI: 10.2174/9789815238488124020021
PDF Price: $30
Introduction
This handbook provides a comprehensive understanding of computational linguistics, focusing on the integration of deep learning in natural language processing (NLP). 18 edited chapters cover the state-of-the-art theoretical and experimental research on NLP, offering insights into advanced models and recent applications. Highlights: - Foundations of NLP: Provides an in-depth study of natural language processing, including basics, challenges, and applications. - Advanced NLP Techniques: Explores recent advancements in text summarization, machine translation, and deep learning applications in NLP. - Practical Applications: Demonstrates use cases on text identification from hazy images, speech-to-sign language translation, and word sense disambiguation using deep learning. - Future Directions: Includes discussions on the future of NLP, including transfer learning, beyond syntax and semantics, and emerging challenges. Key Features: - Comprehensive coverage of NLP and deep learning integration. - Practical insights into real-world applications - Detailed exploration of recent research and advancements through 16 easy to read chapters - References and notes on experimental methods used for advanced readers Ideal for researchers, students, and professionals, this book offers a thorough understanding of computational linguistics by equipping readers with the knowledge to understand how computational techniques are applied to understand text, language and speech.