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| nlp question answering: Hands-on Question Answering Systems with BERT Navin Sabharwal, Amit Agrawal, 2021-02-06 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. |
| nlp question answering: Open-Domain Question Answering John Prager, 2007 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. |
| nlp question answering: Computational Linguistics and Intelligent Text Processing Alexander Gelbukh, 2009-02-16 This book constitutes the refereed proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2009, held in Mexico City, Mexico in March 2009. The 44 revised full papers presented together with 4 invited papers were carefully reviewed and selected from numerous submissions. The papers cover all current issues in computational linguistics research and present intelligent text processing applications. |
| nlp question answering: Dependency Parsing Sandra Kübler, Ryan McDonald, Joakim Nivre, 2022-05-31 Dependency-based methods for syntactic parsing have become increasingly popular in natural language processing in recent years. This book gives a thorough introduction to the methods that are most widely used today. After an introduction to dependency grammar and dependency parsing, followed by a formal characterization of the dependency parsing problem, the book surveys the three major classes of parsing models that are in current use: transition-based, graph-based, and grammar-based models. It continues with a chapter on evaluation and one on the comparison of different methods, and it closes with a few words on current trends and future prospects of dependency parsing. The book presupposes a knowledge of basic concepts in linguistics and computer science, as well as some knowledge of parsing methods for constituency-based representations. Table of Contents: Introduction / Dependency Parsing / Transition-Based Parsing / Graph-Based Parsing / Grammar-Based Parsing / Evaluation / Comparison / Final Thoughts |
| nlp question answering: The Oxford Handbook of Computational Linguistics Ruslan Mitkov, 2004 This handbook of computational linguistics, written for academics, graduate students and researchers, provides a state-of-the-art reference to one of the most active and productive fields in linguistics. |
| nlp question answering: 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave) IEEE Staff, 2016-02-29 Information and communication technology Human Development Food, Agriculture, Education, Entrepreneurship & Health Infrastructure Telecom and Rural Infra Energy and Environment Critical technologies and Strategic Industries |
| nlp question answering: Natural Language Processing with Transformers, Revised Edition Lewis Tunstall, Leandro von Werra, Thomas Wolf, 2022-05-26 Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments |
| nlp question answering: Advances in Open Domain Question Answering Tomek Strzalkowski, Sanda Harabagiu, 2006-10-07 Automated question answering - the ability of a machine to answer questions, simple or complex, posed in ordinary human language - is one of today’s most exciting technological developments. It has all the markings of a disruptive technology, one that is poised to displace the existing search methods and establish new standards for user-centered access to information. This book gives a comprehensive and detailed look at the 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 advanced graduate courses in Computer Science, Information Science and related disciplines. The readers will acquire in-depth practical knowledge of this critical new technology. |
| nlp question answering: Memory-Based Parsing Sandra Kübler, 2004-10-31 Memory-Based Learning (MBL), one of the most influential machine learning paradigms, has been applied with great success to a variety of NLP tasks. This monograph describes the application of MBL to robust parsing. Robust parsing using MBL can provide added functionality for key NLP applications, such as Information Retrieval, Information Extraction, and Question Answering, by facilitating more complex syntactic analysis than is currently available. The text presupposes no prior knowledge of MBL. It provides a comprehensive introduction to the framework and goes on to describe and compare applications of MBL to parsing. Since parsing is not easily characterizable as a classification task, adaptations of standard MBL are necessary. These adaptations can either take the form of a cascade of local classifiers or of a holistic approach for selecting a complete tree.The text provides excellent course material on MBL. It is equally relevant for any researcher concerned with symbolic machine learning, Information Retrieval, Information Extraction, and Question Answering. |
| nlp question answering: Business Intelligence and Information Technology Aboul Ella Hassanien, Yaoqun Xu, Zhijie Zhao, Sabah Mohammed, Zhipeng Fan, 2021-12-15 This book constitutes the refereed proceedings of the 2021 International Conference on Business Intelligence and Information Technology (BIIT 2021) held in Harbin, China, during December 18–20, 2021. BIIT 2021 is organized by the School of Computer and Information Engineering, Harbin University of Commerce, and supported by Scientific Research Group in Egypt (SRGE), Egypt. The papers cover current research in electronic commerce technology and application, business intelligence and decision making, digital economy, accounting informatization, intelligent information processing, image processing and multimedia technology, signal detection and processing, communication engineering and technology, information security, automatic control technique, data mining, software development, and design, blockchain technology, big data technology, artificial intelligence technology. |
| nlp question answering: Sentic Computing Erik Cambria, Amir Hussain, 2012-07-28 In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. |
| nlp question answering: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| nlp question answering: Advances in Information Communication Technology and Computing Vishal Goar, Manoj Kuri, Rajesh Kumar, Tomonobu Senjyu, 2020-08-18 This book features selected research papers presented at the International Conference on Advances in Information Communication Technology and Computing (AICTC 2019), held at the Government Engineering College Bikaner, Bikaner, India, on 8–9 November 2019. It covers ICT-based approaches in the areas ICT for energy efficiency, life cycle assessment of ICT, green IT, green information systems, environmental informatics, energy informatics, sustainable HCI and computational sustainability. |
| nlp question answering: Handbook of Linguistic Annotation Nancy Ide, James Pustejovsky, 2017-06-16 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 automatic annotation process, evaluation, and iterative improvement of annotation accuracy. The second part of the book includes case studies of annotation projects across the spectrum of linguistic annotation types, including morpho-syntactic tagging, syntactic analyses, a range of semantic analyses (semantic roles, named entities, sentiment and opinion), time and event and spatial analyses, and discourse level analyses including discourse structure, co-reference, etc. Each case study addresses the various phases and processes discussed in the chapters of part one. |
| nlp question answering: Emerging Applications of Natural Language Processing: Concepts and New Research Bandyopadhyay, Sivaji, 2012-10-31 This book provides pertinent and vital information that researchers, postgraduate, doctoral students, and practitioners are seeking for learning about the latest discoveries and advances in NLP methodologies and applications of NLP--Provided by publisher. |
| nlp question answering: Soft Computing for Problem Solving Jagdish Chand Bansal, 2019 This two-volume book presents outcomes of the 7th International Conference on Soft Computing for Problem Solving, SocProS 2017. This conference is a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), the Indian Institute of Technology Roorkee, the South Asian University New Delhi and the National Institute of Technology Silchar, and brings together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions The book presents the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers in the areas including, but not limited to, algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It is a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task. |
| nlp question answering: New Directions in Question Answering Mark T. Maybury, 2004 Major trends in the development of an important new method of information access that combines elements of natural language processing, information retrieval, and human computer interaction. Question answering systems, which provide natural language responses to natural language queries, are the subject of rapidly advancing research encompassing both academic study and commercial applications, the most well-known of which is the search engine Ask Jeeves. Question answering draws on different fields and technologies, including natural language processing, information retrieval, explanation generation, and human computer interaction. Question answering creates an important new method of information access and can be seen as the natural step beyond such standard Web search methods as keyword query and document retrieval. This collection charts significant new directions in the field, including temporal, spatial, definitional, biographical, multimedia, and multilingual question answering. After an introduction that defines essential terminology and provides a roadmap to future trends, the book covers key areas of research and development. These include current methods, architecture requirements, and the history of question answering on the Web; the development of systems to address new types of questions; interactivity, which is often required for clarification of questions or answers; reuse of answers; advanced methods; and knowledge representation and reasoning used to support question answering. Each section contains an introduction that summarizes the chapters included and places them in context, relating them to the other chapters in the book as well as to the existing literature in the field and assessing the problems and challenges that remain. |
| nlp question answering: Mathematics for Machine Learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020-04-23 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. |
| nlp question answering: Experimental IR Meets Multilinguality, Multimodality, and Interaction Avi Arampatzis, Evangelos Kanoulas, Theodora Tsikrika, Stefanos Vrochidis, Hideo Joho, Christina Lioma, Carsten Eickhoff, Aurélie Névéol, Linda Cappellato, Nicola Ferro, 2020-09-15 This book constitutes the refereed proceedings of the 11th International Conference of the CLEF Association, CLEF 2020, held in Thessaloniki, Greece, in September 2020.* The conference has a clear focus on experimental information retrieval with special attention to the challenges of multimodality, multilinguality, and interactive search ranging from unstructured to semi structures and structured data. The 5 full papers and 2 short papers presented in this volume were carefully reviewed and selected from 9 submissions. This year, the contributions addressed the following challenges: a large-scale evaluation of translation effects in academic search, advancement of assessor-driven aggregation methods for efficient relevance assessments, and development of a new test dataset. In addition to this, the volume presents 7 “best of the labs” papers which were reviewed as full paper submissions with the same review criteria. The 12 lab overview papers were accepted out of 15 submissions and represent scientific challenges based on new data sets and real world problems in multimodal and multilingual information access. * The conference was held virtually due to the COVID-19 pandemic. |
| nlp question answering: Positive Intelligence Shirzad Chamine, 2012 Chamine exposes how your mind is sabotaging you and keeping your from achieving your true potential. He shows you how to take concrete steps to unleash the vast, untapped powers of your mind. |
| nlp question answering: Recent Advances in Natural Language Processing III Nicolas Nicolov, 2004 This volume brings together revised versions of a selection of papers presented at the 2003 International Conference on Recent Advances in Natural Language Processing. A wide range of topics is covered in the volume: semantics, dialog, summarization, anaphora resolution, shallow parsing, morphology, part-of-speech tagging, named entity, question answering, word sense disambiguation, information extraction. Various 'state-of-the-art' techniques are explored: finite state processing, machine learning (support vector machines, maximum entropy, decision trees, memory-based learning, inductive logic programming, transformation-based learning, perceptions), latent semantic analysis, constraint programming. The papers address different languages (Arabic, English, German, Slavic languages) and use different linguistic frameworks (HPSG, LFG, constraint-based DCG). This book will be of interest to those who work in computational linguistics, corpus linguistics, human language technology, translation studies, cognitive science, psycholinguistics, artificial intelligence, and informatics. |
| nlp question answering: Computational Science and Technology Rayner Alfred, Hiroyuki Iida, Haviluddin Haviluddin, Patricia Anthony, 2021-03-15 This book gathers the proceedings of the Seventh International Conference on Computational Science and Technology 2020 (ICCST 2020), held in Pattaya, Thailand, on 29–30 August 2020. The respective contributions offer practitioners and researchers a range of new computational techniques and solutions, identify emerging issues, and outline future research directions, while also showing them how to apply the latest large-scale, high-performance computational methods. |
| nlp question answering: Development of IR Evaluation Methods Stephen Edward Robertson, 1999 |
| nlp question answering: Extended Finite State Models of Language Andras Kornai, 1999-09-13 This book and CD-ROM cover the breadth of contemporary finite state language modeling, from mathematical foundations to developing and debugging specific grammars. |
| nlp question answering: Natural Language Processing with Python Steven Bird, Ewan Klein, Edward Loper, 2009-06-12 This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify named entities Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful. |
| nlp question answering: The NIPS '17 Competition: Building Intelligent Systems Sergio Escalera, Markus Weimer, 2018-09-27 This book summarizes the organized competitions held during the first NIPS competition track. It provides both theory and applications of hot topics in machine learning, such as adversarial learning, conversational intelligence, and deep reinforcement learning. Rigorous competition evaluation was based on the quality of data, problem interest and impact, promoting the design of new models, and a proper schedule and management procedure. This book contains the chapters from organizers on competition design and from top-ranked participants on their proposed solutions for the five accepted competitions: The Conversational Intelligence Challenge, Classifying Clinically Actionable Genetic Mutations, Learning to Run, Human-Computer Question Answering Competition, and Adversarial Attacks and Defenses. |
| nlp question answering: Speech and Language Processing Daniel Jurafsky, James H. Martin, 2000-01 This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing. |
| nlp question answering: Getting Started with Google BERT Sudharsan Ravichandiran, 2021-01-22 Kickstart your NLP journey by exploring BERT and its variants such as ALBERT, RoBERTa, DistilBERT, VideoBERT, and more with Hugging Face's transformers library Key Features Explore the encoder and decoder of the transformer model Become well-versed with BERT along with ALBERT, RoBERTa, and DistilBERT Discover how to pre-train and fine-tune BERT models for several NLP tasks Book Description BERT (bidirectional encoder representations from transformer) has revolutionized the world of natural language processing (NLP) with promising results. This book is an introductory guide that will help you get to grips with Google's BERT architecture. With a detailed explanation of the transformer architecture, this book will help you understand how the transformer's encoder and decoder work. You'll explore the BERT architecture by learning how the BERT model is pre-trained and how to use pre-trained BERT for downstream tasks by fine-tuning it for NLP tasks such as sentiment analysis and text summarization with the Hugging Face transformers library. As you advance, you'll learn about different variants of BERT such as ALBERT, RoBERTa, and ELECTRA, and look at SpanBERT, which is used for NLP tasks like question answering. You'll also cover simpler and faster BERT variants based on knowledge distillation such as DistilBERT and TinyBERT. The book takes you through MBERT, XLM, and XLM-R in detail and then introduces you to sentence-BERT, which is used for obtaining sentence representation. Finally, you'll discover domain-specific BERT models such as BioBERT and ClinicalBERT, and discover an interesting variant called VideoBERT. By the end of this BERT book, you'll be well-versed with using BERT and its variants for performing practical NLP tasks. What You Will Learn Understand the transformer model from the ground up Find out how BERT works and pre-train it using masked language model (MLM) and next sentence prediction (NSP) tasks Get hands-on with BERT by learning to generate contextual word and sentence embeddings Fine-tune BERT for downstream tasks Get to grips with ALBERT, RoBERTa, ELECTRA, and SpanBERT models Get the hang of the BERT models based on knowledge distillation Understand cross-lingual models such as XLM and XLM-R Explore Sentence-BERT, VideoBERT, and BART Who this book is for This book is for NLP professionals and data scientists looking to simplify NLP tasks to enable efficient language understanding using BERT. A basic understanding of NLP concepts and deep learning is required to get the best out of this book. |
| nlp question answering: Practical Natural Language Processing Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana, 2020-06-17 Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective |
| nlp question answering: Representation Learning for Natural Language Processing Zhiyuan Liu, Yankai Lin, Maosong Sun, 2020-07-03 This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. |
| nlp question answering: Artificial Intelligence Stuart Jonathan Russell, Peter Norvig, 2013-07-31 In this third edition, the authors have updated the treatment of all major areas. A new organizing principle--the representational dimension of atomic, factored, and structured models--has been added. Significant new material has been provided in areas such as partially observable search, contingency planning, hierarchical planning, relational and first-order probability models, regularization and loss functions in machine learning, kernel methods, Web search engines, information extraction, and learning in vision and robotics. The book also includes hundreds of new exercises. |
| nlp question answering: Natural Language Processing Dr.S.Jothi Lakshmi, Dr.S.Suguna Devi, Dr.T.R.Ramesh, Dr.S.Ashok Kumar, Mr.P.Radhakrishnan, 2023-12-08 Dr.S.JOTHI LAKSHMI, Assistant Professor, Department of Computer Science, The Standard Fireworks Rajaratnam College for Women, Sivakasi, Tamil Nadu, India. Dr.S.SUGUNA DEVI, Associate Professor, Department of Information Technology, Cauvery College for Women (Autonomous), Tiruchirappalli, Tamil Nadu, India. Dr.T.R.RAMESH, Assistant Professor, Department of Computer Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India. Dr.S.ASHOKKUMAR, Professor, Department of Cyber Security, Institute of Computer Science and Engineering, Saveetha School of Engineering (Saveetha University), Thandalam, Chennai, Tamil Nadu, India. Mr.P.RADHAKRISHNAN, Assistant Professor, School of Computer Science & Artificial Intelligence, SR University, Warangal, Telangana, India. |
| nlp question answering: Question Answering for the Curated Web Rishiraj Saha Roy, Avishek Anand, 2022-05-31 Question answering (QA) systems on the Web try to provide crisp answers to information needs posed in natural language, replacing the traditional ranked list of documents. QA, posing a multitude of research challenges, has emerged as one of the most actively investigated topics in information retrieval, natural language processing, and the artificial intelligence communities today. The flip side of such diverse and active interest is that publications are highly fragmented across several venues in the above communities, making it very difficult for new entrants to the field to get a good overview of the topic. Through this book, we make an attempt towards mitigating the above problem by providing an overview of the state-of-the-art in question answering. We cover the twin paradigms of curated Web sources used in QA tasks ‒ trusted text collections like Wikipedia, and objective information distilled into large-scale knowledge bases. We discuss distinct methodologies that have been applied to solve the QA problem in both these paradigms, using instantiations of recent systems for illustration. We begin with an overview of the problem setup and evaluation, cover notable sub-topics like open-domain, multi-hop, and conversational QA in depth, and conclude with key insights and emerging topics. We believe that this resource is a valuable contribution towards a unified view on QA, helping graduate students and researchers planning to work on this topic in the near future. |
| nlp question answering: Natural Language Question Answering Systems Leonard Bolc, 1980 |
| nlp question answering: Applied Natural Language Processing in the Enterprise Ankur A Patel, Ajay Uppili Arasanipalai, 2021-04-13 NLP is one of the hottest topics in AI today. Having lagged for years behind other deep learning fields such as computer vision, NLP only recently gained mainstream popularity. Google, Facebook, and OpenAI have open-sourced large pretrained language models, but many organizations today still struggle with building and adopting NLP applications. This hands-on guide helps you learn the process quickly. If you have a basic to intermediate understanding of machine learning and programming experience with Python, you'll learn how to build and deploy real-world NLP applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai walk you through the process without bogging you down in theory. Understand how state-of-the-art NLP models work Learn the tools of the trade, including frameworks popular today Perform NLP tasks such as text classification, semantic search, and reading comprehension Solve problems using new models like transformers and techniques such as transfer learning Build NLP models from scratch with performance comparable or superior to out-of-the-box systems Deploy your models to production and maintain their performance Implement a suite of NLP algorithms using Python and PyTorch |
| nlp question answering: Natural Language Processing and Information Systems Farid Meziane, 2004-08-13 This book constitutes the refereed proceedings of the 9th International Conference on Applications of Natural Language to Information Systems, NLDB 2004, held in Salford, UK in June 2004. The 29 revised full papers and 13 revised short papers presented were carefully reviewed and selected from 65 submissions. The papers are organized in topical sections on natural language, conversational systems, intelligent querying, linguistic aspects of modeling, information retrieval, natural language text understanding, knowledge bases, knowledge management and content management. |
| nlp question answering: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) IEEE Staff, 2020-07 THE 11th INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT) aims to provide a forum that brings together International researchers from academia and practitioners in the industry to meet and exchange ideas and recent research work on all aspects of Information and Communication Technologies |
| nlp question answering: 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) IEEE Staff, 2020-10-22 Artificial Intelligence, Autonomous Systems, Big Data Processing, Biomedical Technologies, Biotechnology, Building Technologies, Chemical, Biological, Radiological and Nuclear Defense, Criminal and Forensic Science, Cognitive Systems, Current Issues and Challenges in Innovation, Environmental Chemistry and Toxicology, Fuel Cell and Water Splitter, Geographic Information System, Green Energy and Green Technology, Grid and Cloud Computing, Intellectual Property Rights, Intelligent Communications and Networks, Laser and Photonic, Lean Manufacturing Technologies, Machine Learning Technologies, Material Technologies and Secondary Process, Microfluidics, Nanotechnology and Material Sciences, Nano and MicroElectro Mechanical Systems, Nuclear Science and Techiniques, Polymer Science, Recycling Technologies, Simulation Technologies, Smart Grid, Space Application, Terahertz Spectroscopy and Applications, Weapon and Ammunition Systems , Unmanned Aerial Vehicle, Virtual Reality |
| nlp question answering: The Great Mental Models: General Thinking Concepts Farnam Street, 2019-12-16 The old saying goes, ''To the man with a hammer, everything looks like a nail.'' But anyone who has done any kind of project knows a hammer often isn't enough. The more tools you have at your disposal, the more likely you'll use the right tool for the job - and get it done right. The same is true when it comes to your thinking. The quality of your outcomes depends on the mental models in your head. And most people are going through life with little more than a hammer. Until now. The Great Mental Models: General Thinking Concepts is the first book in The Great Mental Models series designed to upgrade your thinking with the best, most useful and powerful tools so you always have the right one on hand. This volume details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making, productivity, and how clearly you see the world. You will discover what forces govern the universe and how to focus your efforts so you can harness them to your advantage, rather than fight with them or worse yet- ignore them. Upgrade your mental toolbox and get the first volume today. AUTHOR BIOGRAPHY Farnam Street (FS) is one of the world's fastest growing websites, dedicated to helping our readers master the best of what other people have already figured out. We curate, examine and explore the timeless ideas and mental models that history's brightest minds have used to live lives of purpose. Our readers include students, teachers, CEOs, coaches, athletes, artists, leaders, followers, politicians and more. They're not defined by gender, age, income, or politics but rather by a shared passion for avoiding problems, making better decisions, and lifelong learning. AUTHOR HOME Ottawa, Ontario, Canada |
| nlp question answering: Natural Language Processing and Information Retrieval Muskan Garg, Sandeep Kumar, Abdul Khader Jilani Saudagar, 2023-11-28 This book presents the basics and recent advancements in natural language processing and information retrieval in a single volume. It will serve as an ideal reference text for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. This text emphasizes the existing problem domains and possible new directions in natural language processing and information retrieval. It discusses the importance of information retrieval with the integration of machine learning, deep learning, and word embedding. This approach supports the quick evaluation of real-time data. It covers important topics including rumor detection techniques, sentiment analysis using graph-based techniques, social media data analysis, and language-independent text mining. Features: • Covers aspects of information retrieval in different areas including healthcare, data analysis, and machine translation • Discusses recent advancements in language- and domain-independent information extraction from textual and/or multimodal data • Explains models including decision making, random walk, knowledge graphs, word embedding, n-grams, and frequent pattern mining • Provides integrated approaches of machine learning, deep learning, and word embedding for natural language processing • Covers latest datasets for natural language processing and information retrieval for social media like Twitter The text is primarily written for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. |
Natural language processing - Wikipedia
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence. It is primarily concerned with providing computers with the ability to process data …
Natural Language Processing (NLP) - Overview - GeeksforGeeks
Apr 8, 2025 · Natural Language Processing (NLP) is a field that combines computer science, artificial intelligence and language studies. It helps computers understand, process and create …
What is NLP (natural language processing)? - IBM
4 days ago · NLP enables computers and digital devices to recognize, understand and generate text and speech by combining computational linguistics, the rule-based modeling of human …
Natural Language Processing (NLP) [A Complete Guide]
Jan 11, 2023 · Natural language processing (NLP) is the discipline of building machines that can manipulate human language — or data that resembles human language — in the way that it is …
What is Natural Language Processing (NLP)? A Beginner’s Guide
Feb 13, 2024 · Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. The …
What is Natural Language Processing? Definition and Examples
May 23, 2025 · Natural language processing (NLP) is a subset of artificial intelligence, computer science, and linguistics focused on making human communication, such as speech and text, …
Natural Language Processing (NLP): What it is and why it matters
Natural language processing (NLP) makes it possible for humans to talk to machines. Learn how our devices understand language and how to apply this technology.
Natural language processing (NLP) | Definition, History, & Facts ...
Jun 6, 2025 · natural language processing (NLP), in computer science, the use of operations, systems, and technologies that allow computers to process and respond to written and spoken …
Complete Guide to Natural Language Processing (NLP) – with …
Natural language processing (NLP) is the technique by which computers understand the human language. NLP allows you to perform a wide range of tasks such as classification, …
What is NLP? - Natural Language Processing Explained - AWS
Natural language processing (NLP) techniques, or NLP tasks, break down human text or speech into smaller parts that computer programs can easily understand. Common text processing …
Natural language processing - Wikipedia
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence. It is primarily concerned with providing computers with the ability to process data …
Natural Language Processing (NLP) - Overview - GeeksforGeeks
Apr 8, 2025 · Natural Language Processing (NLP) is a field that combines computer science, artificial intelligence and language studies. It helps computers understand, process and create …
What is NLP (natural language processing)? - IBM
4 days ago · NLP enables computers and digital devices to recognize, understand and generate text and speech by combining computational linguistics, the rule-based modeling of human …
Natural Language Processing (NLP) [A Complete Guide]
Jan 11, 2023 · Natural language processing (NLP) is the discipline of building machines that can manipulate human language — or data that resembles human language — in the way that it is …
What is Natural Language Processing (NLP)? A Beginner’s Guide
Feb 13, 2024 · Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. The …
What is Natural Language Processing? Definition and Examples
May 23, 2025 · Natural language processing (NLP) is a subset of artificial intelligence, computer science, and linguistics focused on making human communication, such as speech and text, …
Natural Language Processing (NLP): What it is and why it matters
Natural language processing (NLP) makes it possible for humans to talk to machines. Learn how our devices understand language and how to apply this technology.
Natural language processing (NLP) | Definition, History, & Facts ...
Jun 6, 2025 · natural language processing (NLP), in computer science, the use of operations, systems, and technologies that allow computers to process and respond to written and spoken …
Complete Guide to Natural Language Processing (NLP) – with …
Natural language processing (NLP) is the technique by which computers understand the human language. NLP allows you to perform a wide range of tasks such as classification, …
What is NLP? - Natural Language Processing Explained - AWS
Natural language processing (NLP) techniques, or NLP tasks, break down human text or speech into smaller parts that computer programs can easily understand. Common text processing …
