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Relazioni semantiche tra nominali [Lezioni di sintesi sulla techno del linguaggio umano-
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- Compralo SubitoRelazioni semantiche tra nominali [Lezioni di sintesi sulla techno del linguaggio umano
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Specifiche dell'oggetto
- Condizione
- Book Title
- Semantic Relations Between Nominals (Synthesis Lectures on Human
- ISBN
- 9781636390888
- Subject Area
- Computers, Language Arts & Disciplines
- Publication Name
- Semantic Relations between Nominals
- Publisher
- Morgan & Claypool Publishers
- Item Length
- 9.3 in
- Subject
- Natural Language Processing, Linguistics / General
- Publication Year
- 2021
- Series
- Synthesis Lectures on Human Language Technologies Ser.
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Features
- New Edition
- Item Width
- 7.5 in
- Number of Pages
- 234 Pages
Informazioni su questo prodotto
Product Identifiers
Publisher
Morgan & Claypool Publishers
ISBN-10
1636390889
ISBN-13
9781636390888
eBay Product ID (ePID)
2328304398
Product Key Features
Number of Pages
234 Pages
Language
English
Publication Name
Semantic Relations between Nominals
Publication Year
2021
Subject
Natural Language Processing, Linguistics / General
Features
New Edition
Type
Textbook
Subject Area
Computers, Language Arts & Disciplines
Series
Synthesis Lectures on Human Language Technologies Ser.
Format
Hardcover
Dimensions
Item Length
9.3 in
Item Width
7.5 in
Additional Product Features
Edition Number
2
Intended Audience
Scholarly & Professional
Dewey Edition
23
Dewey Decimal
418.020285635
Edition Description
New Edition
Table Of Content
Preface to the Second Edition Introduction Relations Between Nominals, Relations Between Concepts Extracting Semantic Relations with Supervision Extracting Semantic Relations with Little or No Supervision Semantic Relations and Deep Learning Conclusion Bibliography Authors' Biographies Index
Synopsis
Opportunity and Curiosity find similar rocks on Mars. One can generally understand this statement if one knows that Opportunity and Curiosity are instances of the class of Mars rovers, and recognizes that, as signalled by the word on, ROCKS are located on Mars. Two mental operations contribute to understanding: recognize how entities/concepts mentioned in a text interact and recall already known facts (which often themselves consist of relations between entities/concepts). Concept interactions one identifies in the text can be added to the repository of known facts, and aid the processing of future texts. The amassed knowledge can assist many advanced language-processing tasks, including summarization, question answering and machine translation. Semantic relations are the connections we perceive between things which interact. The book explores two, now intertwined, threads in semantic relations: how they are expressed in texts and what role they play in knowledge repositories. A historical perspective takes us back more than 2000 years to their beginnings, and then to developments much closer to our time: various attempts at producing lists of semantic relations, necessary and sufficient to express the interaction between entities/concepts. A look at relations outside context, then in general texts, and then in texts in specialized domains, has gradually brought new insights, and led to essential adjustments in how the relations are seen. At the same time, datasets which encompass these phenomena have become available. They started small, then grew somewhat, then became truly large. The large resources are inevitably noisy because they are constructed automatically. The available corpora--to be analyzed, or used to gather relational evidence--have also grown, and some systems now operate at the Web scale. The learning of semantic relations has proceeded in parallel, in adherence to supervised, unsupervised or distantly supervised paradigms. Detailed analyses of annotated datasets in supervised learning have granted insights useful in developing unsupervised and distantly supervised methods. These in turn have contributed to the understanding of what relations are and how to find them, and that has led to methods scalable to Web-sized textual data. The size and redundancy of information in very large corpora, which at first seemed problematic, have been harnessed to improve the process of relation extraction/learning. The newest technology, deep learning, supplies innovative and surprising solutions to a variety of problems in relation learning. This book aims to paint a big picture and to offer interesting details., Semantic relations are the connections we perceive between things which interact. This book explores two, now intertwined, threads in semantic relations: how they are expressed in texts and what role they play in knowledge repositories., Opportunity and Curiosity find similar rocks on Mars. One can generally understand this statement if one knows that Opportunity and Curiosity are instances of the class of Mars rovers, and recognizes that, as signalled by the word on, ROCKS are located on Mars. Two mental operations contribute to understanding: recognize how entities/concepts mentioned in a text interact and recall already known facts (which often themselves consist of relations between entities/concepts). Concept interactions one identifies in the text can be added to the repository of known facts, and aid the processing of future texts. The amassed knowledge can assist many advanced language-processing tasks, including summarization, question answering and machine translation. Semantic relations are the connections we perceive between things which interact. The book explores two, now intertwined, threads in semantic relations: how they are expressed in texts and what role they play in knowledge repositories. A historical perspective takes us back more than 2000 years to their beginnings, and then to developments much closer to our time: various attempts at producing lists of semantic relations, necessary and sufficient to express the interaction between entities/concepts. A look at relations outside context, then in general texts, and then in texts in specialized domains, has gradually brought new insights, and led to essential adjustments in how the relations are seen. At the same time, datasets which encompass these phenomena have become available. They started small, then grew somewhat, then became truly large. The large resources are inevitably noisy because they are constructed automatically. The available corpora-to be analyzed, or used to gather relational evidence-have also grown, and some systems now operate at the Web scale. The learning of semantic relations has proceeded in parallel, in adherence to supervised, unsupervised or distantly supervised paradigms. Detailed analyses of annotated datasets in supervised learning have granted insights useful in developing unsupervised and distantly supervised methods. These in turn have contributed to the understanding of what relations are and how to find them, and that has led to methods scalable to Web-sized textual data. The size and redundancy of information in very large corpora, which at first seemed problematic, have been harnessed to improve the process of relation extraction/learning. The newest technology, deep learning, supplies innovative and surprising solutions to a variety of problems in relation learning. This book aims to paint a big picture and to offer interesting details.
LC Classification Number
P309
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