MARC details
000 -LEADER |
fixed length control field |
02450nam a22002537a 4500 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
240126s2022 |||||||| |||| 00| 0 eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
International Standard Book Number |
9783030966256 |
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
ISSN-L |
9783030966256 |
041 ## - LANGUAGE CODE |
Language code of text/sound track or separate title |
English |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
Classification number |
006.31 |
Item number |
AGG |
100 ## - MAIN ENTRY--PERSONAL NAME |
Personal name |
Aggarwal, Charu C. |
9 (RLIN) |
879414 |
Relator term |
author |
245 ## - TITLE STATEMENT |
Title |
Machine Learning for Text |
250 ## - EDITION STATEMENT |
Edition statement |
2nd |
260 ## - PUBLICATION, DISTRIBUTION, ETC. |
Place of publication, distribution, etc. |
Cham, Switzerland : |
Name of publisher, distributor, etc. |
Springer, |
Date of publication, distribution, etc. |
c2022 |
300 ## - PHYSICAL DESCRIPTION |
Extent |
xxiii, 565 p. |
Other physical details |
: ill |
504 ## - BIBLIOGRAPHY, ETC. NOTE |
Bibliography, etc. note |
Includes Bibliographical References and Index |
520 ## - SUMMARY, ETC. |
Summary, etc. |
SUMMARY<br/>his second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.<br/><br/>2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. <br/>3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. <br/><br/>Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
9 (RLIN) |
845 |
Topical term or geographic name entry element |
Machine Learning |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
9 (RLIN) |
150049 |
Topical term or geographic name entry element |
Text Processing Computer Science |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
9 (RLIN) |
844 |
Topical term or geographic name entry element |
Data Mining |
856 ## - ELECTRONIC LOCATION AND ACCESS |
Link text |
TOC |
Uniform Resource Identifier |
<a href="https://eaklibrary.neduet.edu.pk:8443/catalog/bk/books/toc/9783030966256.pdf">https://eaklibrary.neduet.edu.pk:8443/catalog/bk/books/toc/9783030966256.pdf</a> |
856 ## - ELECTRONIC LOCATION AND ACCESS |
Link text |
WEB LINK |
Uniform Resource Identifier |
<a href="https://link.springer.com/book/10.1007/978-3-030-96623-2#toc ">https://link.springer.com/book/10.1007/978-3-030-96623-2#toc </a> |
942 ## - ADDED ENTRY ELEMENTS (KOHA) |
Source of classification or shelving scheme |
Dewey Decimal Classification |
Koha item type |
Book |