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Hands-on Question Answering Systems with BERT
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...
Open-Domain Question Answering
Open-Domain Question Answering is an introduction to the field of Question Answering (QA). It covers the basic principles of QA along with a selection of systems that have exhibited interesting and significant techniques, so it serves more as a tutorial than as an exhaustive survey of the field. Starting with a brief history of the field, it goes on to describe the architecture of a QA system before analysing in detail some of the specific approaches that have been successfully deployed by academia and industry designing and building such systems. Open-Domain Question Answering is both a guide for beginners who are embarking on research in this area, and a useful reference for established researchers and practitioners in this field.
Ontologies and Adaptivity in Dialogue for Question Answering
Question answering (QA) has become one of the fastest growing topics in computational linguistics and information access. To advance research in the area of dialogue-based question answering, we propose a combination of methods from different scientific fields (i.e., Information Retrieval, Dialogue Systems, Semantic Web, and Machine Learning). This book sheds light on adaptable dialogue-based question answering. We demonstrate the technical and computational feasibility of the proposed ideas, the introspective methods in particular, by beginning with an extensive introduction to the dialogical problem domain which motivates the technical implementation. The ideas have been carried out in a mature natural language processing (NLP) system, the SmartWeb dialogue system, which was developed between 2004 and 2007 by partners from academia and industry. We have attempted to make this book a self-containing text and provide an extra section on the interdisciplinary scientific background. The target audience for this book comprises of researchers and students interested in the application potential of semantic technologies for difficult AI tasks such as working dialogue and QA systems.
Advances in Open Domain Question Answering
This new Springer volume provides a comprehensive and detailed look at current approaches to automated question answering. The level of presentation is suitable for newcomers to the field as well as for professionals wishing to study this area and/or to build practical QA systems. The book can serve as a "how-to" handbook for IT practitioners and system developers. It can also be used to teach graduate courses in Computer Science, Information Science and related disciplines.
Machine Learning for Kids
A hands-on, application-based introduction to machine learning and artificial intelligence (AI). Create compelling AI-powered games and applications using the Scratch programming language. AI Made Easy with 13 Projects Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based companion website, you’ll see how easy it is to add machine learning to your own projects. You don’t even need to know how to code! Step by easy step, you’ll discover how mac...
Handbook of Linguistic Annotation
This handbook offers a thorough treatment of the science of linguistic annotation. Leaders in the field guide the reader through the process of modeling, creating an annotation language, building a corpus and evaluating it for correctness. Essential reading for both computer scientists and linguistic researchers.Linguistic annotation is an increasingly important activity in the field of computational linguistics because of its critical role in the development of language models for natural language processing applications. Part one of this book covers all phases of the linguistic annotation process, from annotation scheme design and choice of representation format through both the manual and...
Open-domain Question-answering
The top-performing Question-Answering (QA) systems have been of two types: consistent, solid, well-established and multi-faceted systems that do well year after year, and ones that come out of nowhere employing totally innovative approaches and which out-perform almost everybody else. This article examines both types of system in depth. We establish what a "typical" QA-system looks like, and cover the commonly used approaches by the component modules. Understanding this will enable any proficient system developer to build his own QA-system. Fortunately there are many components available for free from their developers to make this a reasonable expectation for a graduate-level project. We also look at particular systems that have performed well and which employ interesting and innovative approaches.
Advances in Computational Intelligence
The two-volume set LNAI 12468 and 12469 constitutes the proceedings of the 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, held in Mexico City, Mexico, in October 2020. The total of 77 papers presented in these two volumes was carefully reviewed and selected from 186 submissions. The contributions are organized in topical as follows: Part I: machine and deep learning, evolutionary and metaheuristic algorithms, and soft computing. Part II: natural language processing, image processing and pattern recognition, and intelligent applications and robotics.
