Natural language processing (NLP) enables communication between people and computers and automatic translation to enable people to interact easily with others around the world. The extraordinary development of the internet and the explosion of textual data on the web have boosted the development of the natural language processing field and have especially led to the revival of coipus based NLP and linguistics. Computational and technological developments that incorporate natural language are proliferating. Adequate coverage encounters difficult problems related to partiality, under specification, and context-dependency, which are signature features of information in nature and natural languages. Furthermore, agents (humans or computational systems) are information conveyors, interpreters, or participate as components of informational content. Generally, language processing depends on agents’ knowledge, reasoning, perspectives, and interactions.

The aim of this edited book is to foster interactions among researchers and practitioners in NLP, AI, and allied areas. The edited book covers theoretical work, advanced applications, approaches, and techniques for computational models of infonnation and its presentation by language (artificial, human, or natural in other ways). The goal is to promote intelligent natural language processing (NLP) and related models of thought, mental states, reasoning, and other cognitive processes.

The book is organized into thirteen chapters. Chapter 1 presents a review of the entire process of business intelligence, and then brings out insights on how this platform is used in order to undertake decisions by means of social networks.

Chapter 2 deals with the basic concepts of infonnation retrieval (Ш.) systems, their needs, models of infonnation retrieval systems, and other related concepts, like stemming, indexing, etc. The chapter will help scholars and young professionals to get critical infonnation required for developing IR systems.

Chapter 3 explains all fonns of neural machine translation (NMT), with complete translation of a process that involves a neural network, which produces a number of accessible inputs to find the best possible output according to utilization. This kind of translation applies multiple strategies on different stages for translation. Stage one implements the translated based on words with a complete sentence, and Stage 2 implements the translated on a model over the word within the sentence context. The solidity of neural machine translation allows the learning ability over point-to-point bases on the background knowledge input to the predicable target output. The chapter also includes a brief discussion on various kinds of neural machine translation conversion principles, such as like Google neural machine translation (GNMT), open source neural machine translation (OpenNMT), deep neural machine translation (DeepNMT), and so on.

Chapter 4 discusses how natural language processing may be linked to accomplish human-like language processing. Choosing the word during performance may be very deliberate and may not be replaced with actual understanding. The complete perception of natural language processing may be associated with as making a phrase and outlined the input text and conversion of the text into similar language in different fonn, queries about the text, and inference from the text.

A revolution in the transport environment needs-redesigning of the infrastructure so that the production of embedded vehicles can be chained to an embedded traffic management system. This instinctual design of the traffic control and management system can lead to the improvement of the traffic congestion problem. The traffic density can be calculated using a Raspberry Pi microcomputer and a couple of ultrasonic sensors, and the lanes can be operated accordingly. A website can be designed where traffic data can be uploaded and any user can retrieve it. This property will be useful to users for getting real-time information and detection of any road intersection and discover the fastest traffic route.

Chapter 5 proposes an effective technique for generating ontology by adopting the fruitfly optimization algorithm (FOA). The proposed approach proves that the construction of ontology-based information increases the logistics of the system effectively with low operating cost.

Chapter 6 focuses on supervised approaches (rule based and stochastic- based). Under the stochastic approach, an elaborate discussion on POS labeling using the Viterbi algorithm has been done here. We have also discussed various popularly used POS taggers along with their implementation with proper examples to give a vivid idea on how they work.

Chapter 7 discusses the available methodologies to perform and the most familiar algorithms. The applications are not limited to the classification of research papers, healthcare, customer relationship management (CRM), education; it applies in all places where text has been stored as data. As it is already told that information is wealth; text mining (TM) supports using the information to the next level of decision making for business, result analysis for the education sector, and so on. The detailed text mining algorithms are discussed, such as decision trees, neural networks, discovering algorithms, differential evolution, and so on. Applications such as healthcare, banks, social media, customer relationship and all are connected to the text mining. These applications limited where as the usage of text mining are not limited to the above means. In simple terms, wherever the text has been stored as data in the database, it can be used extensively in taking decisions, predicting results, diagnosing patients, increasing sales, and so on.

Chapter 8 discusses the two basic aspects of natural language understanding (NLU) and natural language generation (NLG) that deals with natural language understanding and generation respectively. Further, the current chapter throws light on various aspects of text processing, like morphological analysis, syntax analysis, semantic analysis, lexical analysis, etc.

Chapters 9 discusses the types of phishing attacks. It also focuses on an anti-phishing URL tool, which is used to prevent phishing attacks. The main objective of this chapter is to explain initially the characteristics of phishing attacks. There are some uniqueness and patterns associated with the websites that are used for phishing. Their properties can be used to detect phishing. Then these attacks are detected by a hybrid machine learning model. The system has been implemented by examining the URLs used in phishing attacks with some extracted features before opening them. Some natural language processing techniques are used in the proposed machine learning system. These techniques are used for analyzing the text semantically to detect malicious intentions that indicate phishing attacks. In order to identify the websites for their legitimacy, some machine learning algorithms (LAs) are also discussed in this chapter. It also focuses on Naive Bayes (NB) classifier and К-Means clustering to calculate the possibility of the website as valid phish or invalid phish.

Chapter 10 discusses that natural language processing is subfield of artificial intelligence (AI) and a research area in the field of computer science recently. It is processed by the computer system and understands the concept that is given as text input and generates some meaningful result. There are many subfields of natural language processing, like machine translation, information retrieval, information extraction (IE), and question answering. There are different types of natural language are available in India, even if in worldwide label like Hindi, Odia, Bengali, Marathi, French, Spanish, and German etc.

We are sincerely thankful to Almighty for supporting and standing at all times with us, whether it’s good or tough times and given ways to conceded us.

Starting from the call for chapters till the finalization of chapters, all the editors have given their contributions amicably, which it a positive sign of significant team works. The editors are sincerely thankful to all the members of Apple Academic Press, especially Sandra Jones Sickels for the providing constructive input and allowing opportunity to edit this important book. We are equally thankful to a reviewers who hail from different places in and around the globe and who shared their support and who stand firm towards quality chapters. The rate of acceptance we have kept as low as 16% to ensure the quality of work submitted by author.

The aim of this book is to support the computational studies at the research and post-graduation level with open problem solving teclmiques. We are confident that it will bridge the gap by supporting novel solutions to support them in their problem solving. At the end, the editors have the taken utmost care while finalizing the chapters for the book, but we are open to receive your constructive feedback, which will enable us to canyout necessary points in our forthcoming books.

—Brojo Kishore Mislira, PhD Raghvendra Kumar, PhD

A Survey on Social Business Intelligence: A Case Study of Application of Dynamic Social Networks for Decision Making


Research Scholar, MAKAUT, and Annex College, Kolkata, West Bengal, India, E-mail: This email address is being protected from spam bots, you need Javascript enabled to view it

  • 2Dr. В C Roy Engineering College, Durgapur, West Bengal, India
  • 3Amity University, Kolkata, West Bengal, India


Over the years, the popularity of social network platforms has increased alarmingly as they are castoff by people for expression of thoughts. Platforms are also used by firms for acknowledgment of numerous opportunities which would lead to the fulfillment of their objectives. The demand for information by firms brings out its fundamental inclinations and dependences that would largely affect performance of the firms. Business intelligence systems are used for obtaining these perceptions. The systems aimed in the derivation of actionable infonnation from social media platforms for the provision of managerial decision-making are demonstrated as social business intelligence (SBI) systems. Numerous queries being raised ranging from ways firms process the external data, the management information derived from new data sources and successful implication of the SBI. In this chapter, we had presented a review of the entire process of business intelligence and then brought out insights on how this platform is used in order to undertake any decision by means of the social network. This work is purely a review paper.

< Prev   CONTENTS   Source   Next >