國立虎尾科技大學 |

A Practical Guide to Hybrid Natural Language Processing = Combining Neural Models and Knowledge Graphs for NLP /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A Practical Guide to Hybrid Natural Language Processing/ by Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva.
其他題名:	Combining Neural Models and Knowledge Graphs for NLP /
作者:	Gomez-Perez, Jose Manuel.
其他作者:	Garcia-Silva, Andres.
面頁冊數:	XXV, 268 p. 376 illus., 9 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Natural Language Processing (NLP). -
電子資源:	https://doi.org/10.1007/978-3-030-44830-1
ISBN:	9783030448301

A Practical Guide to Hybrid Natural Language Processing = Combining Neural Models and Knowledge Graphs for NLP /
Gomez-Perez, Jose Manuel.

A Practical Guide to Hybrid Natural Language ProcessingCombining Neural Models and Knowledge Graphs for NLP /[electronic resource] :by Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva. - 1st ed. 2020. - XXV, 268 p. 376 illus., 9 illus. in color.online resource.

Part I: Preliminaries and Building Blocks -- Hybrid Natural Language Processing: An Introduction -- Word, Sense, and Graph Embeddings -- UnderstandingWord Embeddings and Language Models -- Capturing Meaning from Text asWord Embeddings -- Capturing Knowledge Graph Embeddings -- Part II: Combining Neural Architectures and Knowledge Graphs -- Building Hybrid Representations from Text Corpora, Knowledge Graphs, and Language Models -- Quality Evaluation -- Capturing Lexical, Grammatical, and Semantic Information with Vecsigrafo -- Aligning Embedding Spaces and Applications for Knowledge Graphs -- Part III: Applications -- A Hybrid Approach to Disinformation Analysis -- Jointly Learning Text and Visual Information in the Scientific Domain -- Looking into the Future of Natural Language Processing.

This book provides readers with a practical guide to the principles of hybrid approaches to natural language processing (NLP) involving a combination of neural methods and knowledge graphs. To this end, it first introduces the main building blocks and then describes how they can be integrated to support the effective implementation of real-world NLP applications. To illustrate the ideas described, the book also includes a comprehensive set of experiments and exercises involving different algorithms over a selection of domains and corpora in various NLP tasks. Throughout, the authors show how to leverage complementary representations stemming from the analysis of unstructured text corpora as well as the entities and relations described explicitly in a knowledge graph, how to integrate such representations, and how to use the resulting features to effectively solve NLP tasks in a range of domains. In addition, the book offers access to executable code with examples, exercises and real-world applications in key domains, like disinformation analysis and machine reading comprehension of scientific literature. All the examples and exercises proposed in the book are available as executable Jupyter notebooks in a GitHub repository. They are all ready to be run on Google Colaboratory or, if preferred, in a local environment. A valuable resource for anyone interested in the interplay between neural and knowledge-based approaches to NLP, this book is a useful guide for readers with a background in structured knowledge representations as well as those whose main approach to AI is fundamentally based on logic. Further, it will appeal to those whose main background is in the areas of machine and deep learning who are looking for ways to leverage structured knowledge bases to optimize results along the NLP downstream.

ISBN: 9783030448301

Standard No.: 10.1007/978-3-030-44830-1doiSubjects--Topical Terms:

1254293
Natural Language Processing (NLP).

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

A Practical Guide to Hybrid Natural Language Processing = Combining Neural Models and Knowledge Graphs for NLP /
LDR:04078nam a22003975i 4500 001 1028675
003 DE-He213
005 20200629144928.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030448301 $9 978-3-030-44830-1
024 7 $a 10.1007/978-3-030-44830-1 $2 doi
035 $a 978-3-030-44830-1
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Gomez-Perez, Jose Manuel. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1002554
245 1 2 $a A Practical Guide to Hybrid Natural Language Processing $h [electronic resource] : $b Combining Neural Models and Knowledge Graphs for NLP / $c by Jose Manuel Gomez-Perez, Ronald Denaux, Andres Garcia-Silva.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XXV, 268 p. 376 illus., 9 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Part I: Preliminaries and Building Blocks -- Hybrid Natural Language Processing: An Introduction -- Word, Sense, and Graph Embeddings -- UnderstandingWord Embeddings and Language Models -- Capturing Meaning from Text asWord Embeddings -- Capturing Knowledge Graph Embeddings -- Part II: Combining Neural Architectures and Knowledge Graphs -- Building Hybrid Representations from Text Corpora, Knowledge Graphs, and Language Models -- Quality Evaluation -- Capturing Lexical, Grammatical, and Semantic Information with Vecsigrafo -- Aligning Embedding Spaces and Applications for Knowledge Graphs -- Part III: Applications -- A Hybrid Approach to Disinformation Analysis -- Jointly Learning Text and Visual Information in the Scientific Domain -- Looking into the Future of Natural Language Processing.
520 $a This book provides readers with a practical guide to the principles of hybrid approaches to natural language processing (NLP) involving a combination of neural methods and knowledge graphs. To this end, it first introduces the main building blocks and then describes how they can be integrated to support the effective implementation of real-world NLP applications. To illustrate the ideas described, the book also includes a comprehensive set of experiments and exercises involving different algorithms over a selection of domains and corpora in various NLP tasks. Throughout, the authors show how to leverage complementary representations stemming from the analysis of unstructured text corpora as well as the entities and relations described explicitly in a knowledge graph, how to integrate such representations, and how to use the resulting features to effectively solve NLP tasks in a range of domains. In addition, the book offers access to executable code with examples, exercises and real-world applications in key domains, like disinformation analysis and machine reading comprehension of scientific literature. All the examples and exercises proposed in the book are available as executable Jupyter notebooks in a GitHub repository. They are all ready to be run on Google Colaboratory or, if preferred, in a local environment. A valuable resource for anyone interested in the interplay between neural and knowledge-based approaches to NLP, this book is a useful guide for readers with a background in structured knowledge representations as well as those whose main approach to AI is fundamentally based on logic. Further, it will appeal to those whose main background is in the areas of machine and deep learning who are looking for ways to leverage structured knowledge bases to optimize results along the NLP downstream.
650 2 4 $a Natural Language Processing (NLP). $3 1254293
650 2 4 $a Information Systems Applications (incl. Internet). $3 881699
650 1 4 $a Artificial Intelligence. $3 646849
650 0 $a Natural language processing (Computer science). $3 802180
650 0 $a Application software. $3 528147
650 0 $a Artificial intelligence. $3 559380
700 1 $a Garcia-Silva, Andres. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1325252
700 1 $a Denaux, Ronald. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1325251
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030448295
776 0 8 $i Printed edition: $z 9783030448318
776 0 8 $i Printed edition: $z 9783030448325
856 4 0 $u https://doi.org/10.1007/978-3-030-44830-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)