A Handbook of Computational Linguistics: Artificial Intelligence in Natural Language Processing

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.

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.

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 (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.

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.

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.

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.

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 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 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.

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.

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.

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 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.

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 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.

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.

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.