Natural language question answering systems
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Natural language question answering systems

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Published by C. Hanser, Macmillan Press in München, London, New York .
Written in English

Subjects:

  • Question-answering systems -- Addresses, essays, lectures.

Book details:

Edition Notes

Includes bibliographies.

Statementedited by Leonard Bolc.
SeriesNatural communication with computers
ContributionsBolc, Leonard, 1934-
Classifications
LC ClassificationsQA76.9.Q4 N37 1980
The Physical Object
Pagination305 p. :
Number of Pages305
ID Numbers
Open LibraryOL3520332M
ISBN 100333295285
LC Control Number82104071

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Natural Language Question Answering system [Boris Galitsky] on cyrusofficial.com *FREE* shipping on qualifying offers. The first chapter of this book describes the process of representing the meaning of an input query in the constructed formal language. Following the rule-based semantic analysis for natural langauge questions and definitionsAuthor: Boris Galitsky. Natural Language Processing, or NLP for short, is the study of computational methods for working with speech and text data. The field is dominated by the statistical paradigm and machine learning methods are used for developing predictive models. In this post, you will discover the top books that you can read to get started with . Jul 06,  · This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from /5(6). Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.. Challenges in natural language processing frequently involve speech.

Techniques described enable answering a natural language question using machine learning-based methods to gather and analyze evidence from web searches. A received natural language question is analyzed to extract query units and to determine a question type, answer type, and/or lexical answer type using rules-based heuristics and/or machine. Recent experiments in programming natural language question-answering systems are reviewed to summarize the methods that have been developed for syntactic, Cited by: Aug 07,  · Datasets for Natural Language Processing. This is a list of datasets/corpora for NLP tasks, in reverse chronological order. Suggestions and pull requests are welcome. The goal is to make this a collaborative effort to maintain an updated list of quality datasets. Areas. Question Answering; Dialogue Systems; Goal-Oriented Dialogue Systems. Oct 26,  · Since the dawn of question answering in s, perhaps, all production-level QA systems are divided into two classes: large-domain retrieval-based approaches and narrow-domain natural language interface to databases. There is one more common appro.

Simmons R.F. () Natural Language Question Answering Systems: In: Banerji R.B., Mesarovic M.D. (eds) Theoretical Approaches to Non-Numerical Problem Solving. Lecture Notes in Operations Research and Mathematical Systems (Economics, Computer Science, Information and Control), vol Springer, Berlin, HeidelbergCited by: START, the world's first Web-based question answering system, has been on-line and continuously operating since December, It has been developed by Boris Katz and his associates of the InfoLab Group at the MIT Computer Science and Artificial Intelligence Laboratory. 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. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from.