This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.

Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning. The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you'll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you'll cover word embedding and their types along with the basics of BERT. After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT. What You Will Learn Examine the fundamentals of word embeddings Apply neural networks and BERT for various NLP tasks Develop a question-answering system from scratch Train question-answering systems for your own data Who This Book Is For AI and machine learning developers and natural language processing developers.

This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence-based learning.

Accompanying continued industrial production and sales of artificial intelligence and expert systems is the risk that difficult and resistant theoretical problems and issues will be ignored. The participants at the Third Tinlap Workshop, whose contributions are contained in Theoretical Issues in Natural Language Processing, remove that risk. They discuss and promote theoretical research on natural language processing, examinations of solutions to current problems, development of new theories, and representations of published literature on the subject. Discussions among these theoreticians in artificial intelligence, logic, psychology, philosophy, and linguistics draw a comprehensive, up-to-date picture of the natural language processing field.

Accompanying continued industrial production and sales of artificial intelligence and expert systems is the risk that difficult and resistant theoretical problems and issues will be ignored. The participants at the Third Tinlap Workshop, whose contributions are contained in Theoretical Issues in Natural Language Processing, remove that risk. They discuss and promote theoretical research on natural language processing, examinations of solutions to current problems, development of new theories, and representations of published literature on the subject. Discussions among these theoreticians in artificial intelligence, logic, psychology, philosophy, and linguistics draw a comprehensive, up-to-date picture of the natural language processing field.

Edited in collaboration with FoLLI, the Association of Logic, Language and Information this book constitutes the refereed proceedings of the Second Interdisciplinary Workshop on Logic, Language, and Meaning, TLLM 2020, held in Tsinghua, China, in December 2020. The 12 full papers together presented were fully reviewed and selected from 40 submissions. Due to COVID-19 the workshop will be held online. The workshop covers a wide range of topics where monotonicity is discussed in the context of logic, causality, belief revision, quantification, polarity, syntax, comparatives, and various semantic phenomena in particular languages.

In light of the rapid rise of new trends and applications in various natural language processing tasks, this book presents high-quality research in the field. Each chapter addresses a common challenge in a theoretical or applied aspect of intelligent natural language processing related to Arabic language. Many challenges encountered during the development of the solutions can be resolved by incorporating language technology and artificial intelligence. The topics covered include machine translation; speech recognition; morphological, syntactic, and semantic processing; information retrieval; text classification; text summarization; sentiment analysis; ontology construction; Arabizi translation; Arabic dialects; Arabic lemmatization; and building and evaluating linguistic resources. This book is a valuable reference for scientists, researchers, and students from academia and industry interested in computational linguistics and artificial intelligence, especially for Arabic linguistics and related areas.

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.

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 deals with "Computer Aided Writing", CAW for short. The contents of that is a sector of Knowledge based technics and Knowledge Management. The role of Knowledge Management in social media, education and Industry 4.0 is out of question. More important is the expectation of combining Knowledge Management and Cognitive Technology, which needs more and more new innovations in this field to face recent problems in social and technological areas. The book is intended to provide an overview of the state of research in this field, show the extent to which computer assistance in writing is already being used and present current research contributions. After a brief introduction into the history of writing and the tools that were created, the current developments are examined on the basis of a formal writing model. Tools such as word processing and content management systems will be discussed in detail. The special form of writing, "journalism", is used to examine the effects of Computer Aided Writing. We dedicate a separate chapter to the topic of research, since it is of essential importance in the writing process. With Knowledge Discovery from Text (KDT) and recommendation systems we enter the field of Knowledge Management in the context of Computer Aided Writing. Finally, we will look at methods for automated text generation before giving a final outlook on future developments.

This book focuses mainly on logical approaches to computational linguistics, but also discusses integrations with other approaches, presenting both classic and newly emerging theories and applications.Decades of research on theoretical work and practical applications have demonstrated that computational linguistics is a distinctively interdisciplinary area. There is convincing evidence that computational approaches to linguistics can benefit from research on the nature of human language, including from the perspective of its evolution. This book addresses various topics in computational theories of human language, covering grammar, syntax, and semantics. The common thread running through the research presented is the role of computer science, mathematical logic and other subjects of mathematics in computational linguistics and natural language processing (NLP). Promoting intelligent approaches to artificial intelligence (AI) and NLP, the book is intended for researchers and graduate students in the field.

This book presents methods and approaches used to identify the true author of a doubtful document or text excerpt. It provides a broad introduction to all text categorization problems (like authorship attribution, psychological traits of the author, detecting fake news, etc.) grounded in stylistic features. Specifically, machine learning models as valuable tools for verifying hypotheses or revealing significant patterns hidden in datasets are presented in detail. Stylometry is a multi-disciplinary field combining linguistics with both statistics and computer science. The content is divided into three parts. The first, which consists of the first three chapters, offers a general introduction to stylometry, its potential applications and limitations. Further, it introduces the ongoing example used to illustrate the concepts discussed throughout the remainder of the book. The four chapters of the second part are more devoted to computer science with a focus on machine learning models. Their main aim is to explain machine learning models for solving stylometric problems. Several general strategies used to identify, extract, select, and represent stylistic markers are explained. As deep learning represents an active field of research, information on neural network models and word embeddings applied to stylometry is provided, as well as a general introduction to the deep learning approach to solving stylometric questions. In turn, the third part illustrates the application of the previously discussed approaches in real cases: an authorship attribution problem, seeking to discover the secret hand behind the nom de plume Elena Ferrante, an Italian writer known worldwide for her My Brilliant Friend's saga; author profiling in order to identify whether a set of tweets were generated by a bot or a human being and in this second case, whether it is a man or a woman; and an exploration of stylistic variations over time using US political speeches covering a period of ca. 230 years. A solutions-based approach is adopted throughout the book, and explanations are supported by examples written in R. To complement the main content and discussions on stylometric models and techniques, examples and datasets are freely available at the author's Github website.

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.

This book constitutes the thoroughly refereed post conference papers of the First International Conference on Blockchain and Trustworthy Systems, Blocksys 2019, held in Guangzhou, China, in December 2019. The 50 regular papers and the 19 short papers were carefully reviewed and selected from 130 submissions. The papers are focus on Blockchain and trustworthy systems can be applied to many fields, such as financial services, social management and supply chain management.

This is the first monograph on the emerging area of linguistic linked data. Presenting a combination of background information on linguistic linked data and concrete implementation advice, it introduces and discusses the main benefits of applying linked data (LD) principles to the representation and publication of linguistic resources, arguing that LD does not look at a single resource in isolation but seeks to create a large network of resources that can be used together and uniformly, and so making more of the single resource. The book describes how the LD principles can be applied to modelling language resources. The first part provides the foundation for understanding the remainder of the book, introducing the data models, ontology and query languages used as the basis of the Semantic Web and LD and offering a more detailed overview of the Linguistic Linked Data Cloud. The second part of the book focuses on modelling language resources using LD principles, describing how to model lexical resources using Ontolex-lemon, the lexicon model for ontologies, and how to annotate and address elements of text represented in RDF. It also demonstrates how to model annotations, and how to capture the metadata of language resources. Further, it includes a chapter on representing linguistic categories. In the third part of the book, the authors describe how language resources can be transformed into LD and how links can be inferred and added to the data to increase connectivity and linking between different datasets. They also discuss using LD resources for natural language processing. The last part describes concrete applications of the technologies: representing and linking multilingual wordnets, applications in digital humanities and the discovery of language resources. Given its scope, the book is relevant for researchers and graduate students interested in topics at the crossroads of natural language processing / computational linguistics and the Semantic Web / linked data. It appeals to Semantic Web experts who are not proficient in applying the Semantic Web and LD principles to linguistic data, as well as to computational linguists who are used to working with lexical and linguistic resources wanting to learn about a new paradigm for modelling, publishing and exploiting linguistic resources.

This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence-based learning.

In light of the rapid rise of new trends and applications in various natural language processing tasks, this book presents high-quality research in the field. Each chapter addresses a common challenge in a theoretical or applied aspect of intelligent natural language processing related to Arabic language. Many challenges encountered during the development of the solutions can be resolved by incorporating language technology and artificial intelligence. The topics covered include machine translation; speech recognition; morphological, syntactic, and semantic processing; information retrieval; text classification; text summarization; sentiment analysis; ontology construction; Arabizi translation; Arabic dialects; Arabic lemmatization; and building and evaluating linguistic resources. This book is a valuable reference for scientists, researchers, and students from academia and industry interested in computational linguistics and artificial intelligence, especially for Arabic linguistics and related areas.

This book deals with "Computer Aided Writing", CAW for short. The contents of that is a sector of Knowledge based technics and Knowledge Management. The role of Knowledge Management in social media, education and Industry 4.0 is out of question. More important is the expectation of combining Knowledge Management and Cognitive Technology, which needs more and more new innovations in this field to face recent problems in social and technological areas. The book is intended to provide an overview of the state of research in this field, show the extent to which computer assistance in writing is already being used and present current research contributions. After a brief introduction into the history of writing and the tools that were created, the current developments are examined on the basis of a formal writing model. Tools such as word processing and content management systems will be discussed in detail. The special form of writing, "journalism", is used to examine the effects of Computer Aided Writing. We dedicate a separate chapter to the topic of research, since it is of essential importance in the writing process. With Knowledge Discovery from Text (KDT) and recommendation systems we enter the field of Knowledge Management in the context of Computer Aided Writing. Finally, we will look at methods for automated text generation before giving a final outlook on future developments.

This book constitutes the proceedings of the 23rd International Conference on Developments in Language Theory, DLT 2019, held in Warsaw, Poland, in August 2019. The 20 full papers presented together with three invited talks were carefully reviewed and selected from 30 submissions. The papers cover the following topics and areas: combinatorial and algebraic properties of words and languages; grammars, acceptors and transducers for strings, trees, graphics, arrays; algebraic theories for automata and languages; codes; efficient text algorithms; symbolic dynamics; decision problems; relationships to complexity theory and logic; picture description and analysis, polyominoes and bidimensional patterns; cryptography; concurrency; celluar automata; bio-inspired computing; quantum computing.

This book constitutes the refereed proceedings of the 24th International Conference on Applications of Natural Language to Information Systems, NLDB 2019, held in Salford, UK, in June 2019. The 21 full papers and 16 short papers were carefully reviewed and selected from 75 submissions. The papers are organized in the following topical sections: argumentation mining and applications; deep learning, neural languages and NLP; social media and web analytics; question answering; corpus analysis; semantic web, open linked data, and ontologies; natural language in conceptual modeling; natural language and ubiquitous computing; and big data and business intelligence.

This book presents multibiometric watermarking techniques for security of biometric data. This book also covers transform domain multibiometric watermarking techniques and their advantages and limitations. The authors have developed novel watermarking techniques with a combination of Compressive Sensing (CS) theory for the security of biometric data at the system database of the biometric system. The authors show how these techniques offer higher robustness, authenticity, better imperceptibility, increased payload capacity, and secure biometric watermarks. They show how to use the CS theory for the security of biometric watermarks before embedding into the host biometric data. The suggested methods may find potential applications in the security of biometric data at various banking applications, access control of laboratories, nuclear power stations, military base, and airports.

The objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model. Authors pay attention to the four main findings of the book : -Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the sentiment analysis by eliminating the redundant features. - Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis. - The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis. - Semantic relations among the words in the text have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis.

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You'll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.