Natural language interfaces provide an easy way to query and interact with data and enable non-technical users to investigate data sets without the need to know a query language. Recent advances in natural language understanding and processing have resulted in a renewed interest in natural language interfaces to data. The main challenges in natural language querying are identifying the entities involved in the user utterance, connecting the different entities in a meaningful way over the underlying data source to interpret user intents, and generating a structured query. There are two main approaches in the literature for interpreting a user's natural language query. The first are rule-based systems that make use of semantic indices, ontologies, and knowledge graphs to identify the entities in the query, understand the intended relationships between those entities, and utilize grammars to generate the target queries. Second are hybrid approaches that utilize both rule-based techniques as well as deep learning models. Conversational interfaces are the next natural step to one-shot natural language querying by exploiting query context between multiple turns of conversation for disambiguation. In this monograph, the authors review the rule-based and hybrid technologies that are used in natural language interfaces and survey the different approaches to natural language querying. They also describe conversational interfaces for data analytics and discuss several benchmarks used for natural language querying research and evaluation. The monograph concludes with discussion on challenges that need to be addressed before these systems can be widely adopted.

The volume of natural language text data has been rapidly increasing over the past two decades, due to factors such as the growth of the Web, the low cost associated with publishing, and the progress on the digitization of printed texts. This growth combined with the proliferation of natural language systems for search and retrieving information provides tremendous opportunities for studying some of the areas where database systems and natural language processing systems overlap. This book explores two interrelated and important areas of overlap: (1) managing natural language data and (2) developing natural language interfaces to databases. It presents relevant concepts and research questions, state-of-the-art methods, related systems, and research opportunities and challenges covering both areas. Relevant topics discussed on natural language data management include data models, data sources, queries, storage and indexing, and transforming natural language text. Under natural language interfaces, it presents the anatomy of these interfaces to databases, the challenges related to query understanding and query translation, and relevant aspects of user interactions. Each of the challenges is covered in a systematic way: first starting with a quick overview of the topics, followed by a comprehensive view of recent techniques that have been proposed to address the challenge along with illustrative examples. It also reviews some notable systems in details in terms of how they address different challenges and their contributions. Finally, it discusses open challenges and opportunities for natural language management and interfaces. The goal of this book is to provide an introduction to the methods, problems, and solutions that are used in managing natural language data and building natural language interfaces to databases. It serves as a starting point for readers who are interested in pursuing additional work on these exciting topics in both academic and industrial environments.

This book presents a comprehensive overview of Natural Language Interfaces to Databases (NLIDBs), an indispensable tool in the ever-expanding realm of data-driven exploration and decision making. After first demonstrating the importance of the field using an interactive ChatGPT session, the book explores the remarkable progress and general challenges faced with real-world deployment of NLIDBs. It goes on to provide readers with a holistic understanding of the intricate anatomy, essential components, and mechanisms underlying NLIDBs and how to build them. Key concepts in representing, querying, and processing structured data as well as approaches for optimizing user queries are established for the reader before their application in NLIDBs is explored. The book discusses text to data through early relevant work on semantic parsing and meaning representation before turning to cutting-edge advancements in how NLIDBs are empowered to comprehend and interpret human languages. Various evaluation methodologies, metrics, datasets and benchmarks that play a pivotal role in assessing the effectiveness of mapping natural language queries to formal queries in a database and the overall performance of a system are explored. The book then covers data to text, where formal representations of structured data are transformed into coherent and contextually relevant human-readable narratives. It closes with an exploration of the challenges and opportunities related to interactivity and its corresponding techniques for each dimension, such as instances of conversational NLIDBs and multi-modal NLIDBs where user input is beyond natural language. This book provides a balanced mixture of theoretical insights, practical knowledge, and real-world applications that will be an invaluable resource for researchers, practitioners, and students eager to explore the fundamental concepts of NLIDBs.

This monograph reviews the rule-based and hybrid technologies that are used in natural language interfaces and surveys the different approaches to natural language querying. It also describes conversational interfaces for data analytics and discusses benchmarks used for natural language querying research and evaluation.

What Is Natural Language User Interface A natural-language user interface is a sort of computer human interface in which linguistic phenomena such as verbs, phrases, and clauses operate as UI controllers for the purpose of producing, selecting, and changing data in software programs. Natural-language user interfaces are becoming increasingly popular. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Natural-language user interface Chapter 2: List of artificial intelligence projects Chapter 3: Natural-language understanding Chapter 4: Question answering Chapter 5: Document retrieval Chapter 6: Outline of natural language processing Chapter 7: Concept search Chapter 8: Natural-language programming Chapter 9: Google Hummingbird Chapter 10: Query understanding (II) Answering the public top questions about natural language user interface. (III) Real world examples for the usage of natural language user interface in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of natural language user interface' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of natural language user interface.

As more processes become data-driven, anyone should be able to gather insights into databases without needing to develop complex computer skills typically required for data analytics software. We propose to design new paradigms in which users rely on their own natural language to analyze and visualize data. To that end, we develop three different approaches (unsupervised, rule-based, and supervised) to infer formal specifications from natural language utterances. Contrary to most other work, we developed these approaches in a low-resource environment using synthetically generated training sets, rather than expensive and labor-intensive expert annotations or crowd-sourced examples. Finally, we conducted a study to compare our proposed paradigm to drag-and-drop mechanisms. Not only does our best-performing model, Alcurve, achieve an 86.3% test accuracy on real user input, it also enables users to be 30% more productive when solving analytical tasks, which further highlights the important improvements in usability language-based interfaces can provide.

Spoken Dialogue Technology provides extensive coverage of spoken dialogue systems, ranging from the theoretical underpinnings of the study of dialogue through to a detailed look at a number of well-established methods and tools for developing spoken dialogue systems. The book enables students and practitioners to design and test dialogue systems using several available development environments and languages, including the CSLU toolkit, VoiceXML, SALT, and XHTML+ voice. This practical orientation is usually available otherwise only in reference manuals supplied with software development kits. The latest research in spoken dialogue systems is presented along with extensive coverage of the most relevant theoretical issues and a critical evaluation of current research prototypes. A dedicated web site containing supplementary materials, code, links to resources will enable readers to develop and test their own systems (). Previously such materials have been difficult to track down, available only on a range of disparate web sites and this web site provides a unique and useful reference source which will prove invaluable.

Natural Language Processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and emulate written or spoken human language. NLP draws from many disciplines including human-generated linguistic rules, machine learning, and deep learning to fill the gap between human communication and machine understanding. The papers included in this special collection demonstrate how NLP can be used to scale the human act of reading, organizing, and quantifying text data.

This volume focuses on natural language processing, artificial intelligence, and allied areas. Natural language processing enables communication between people and computers and automatic translation to facilitate easy interaction with others around the world. This book discusses theoretical work and advanced applications, approaches, and techniques for computational models of information and how it is presented by language (artificial, human, or natural) in other ways. It looks at intelligent natural language processing and related models of thought, mental states, reasoning, and other cognitive processes. It explores the difficult problems and challenges related to partiality, underspecification, and context-dependency, which are signature features of information in nature and natural languages. Key features: Addresses the functional frameworks and workflow that are trending in NLP and AI Looks at the latest technologies and the major challenges, issues, and advances in NLP and AI Explores an intelligent field monitoring and automated system through AI with NLP and its implications for the real world Discusses data acquisition and presents a real-time case study with illustrations related to data-intensive technologies in AI and NLP.