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Semantic Relations Between Nominals [Synthesis Lectures on Human Language Techno
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Specificaties
- Objectstaat
- Book Title
- Semantic Relations Between Nominals (Synthesis Lectures on Human
- ISBN
- 9781608459797
- Subject Area
- Computers, Language Arts & Disciplines
- Publication Name
- Semantic Relations between Nominals
- Publisher
- Morgan & Claypool Publishers
- Item Length
- 9.2 in
- Subject
- Intelligence (Ai) & Semantics, Natural Language Processing, Linguistics / General
- Publication Year
- 2013
- Series
- Synthesis Lectures on Human Language Technologies Ser.
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Height
- 0.2 in
- Item Weight
- 7.9 Oz
- Item Width
- 7.5 in
- Number of Pages
- 119 Pages
Over dit product
Product Identifiers
Publisher
Morgan & Claypool Publishers
ISBN-10
1608459799
ISBN-13
9781608459797
eBay Product ID (ePID)
166335390
Product Key Features
Number of Pages
119 Pages
Language
English
Publication Name
Semantic Relations between Nominals
Subject
Intelligence (Ai) & Semantics, Natural Language Processing, Linguistics / General
Publication Year
2013
Type
Textbook
Subject Area
Computers, Language Arts & Disciplines
Series
Synthesis Lectures on Human Language Technologies Ser.
Format
Trade Paperback
Dimensions
Item Height
0.2 in
Item Weight
7.9 Oz
Item Length
9.2 in
Item Width
7.5 in
Additional Product Features
Dewey Edition
23
Dewey Decimal
418.020285635
Table Of Content
Introduction Relations between Nominals, Relations between Concepts Extracting Semantic Relations with Supervision Extracting Semantic Relations with Little or No Supervision Conclusion
Synopsis
People make sense of a text by identifying the semantic relations which connect the entities or concepts described by that text. A system which aspires to human-like performance must also be equipped to identify, and learn from, semantic relations in the texts it processes. Understanding even a simple sentence such as "Opportunity and Curiosity find similar rocks on Mars" requires recognizing relations (rocks are located on Mars, signalled by the word on) and drawing on already known relations (Opportunity and Curiosity are instances of the class of Mars rovers). A language-understanding system should be able to find such relations in documents and progressively build a knowledge base or even an ontology. Resources of this kind assist continuous learning and other advanced language-processing tasks such as text summarization, question answering and machine translation. The book discusses the recognition in text of semantic relations which capture interactions between base noun phrases. After a brief historical background, we introduce a range of relation inventories of varying granularity, which have been proposed by computational linguists. There is also variation in the scale at which systems operate, from snippets all the way to the whole Web, and in the techniques of recognizing relations in texts, from full supervision through weak or distant supervision to self-supervised or completely unsupervised methods. A discussion of supervised learning covers available datasets, feature sets which describe relation instances, and successful algorithms. An overview of weakly supervised and unsupervised learning zooms in on the acquisition of relations from large corpora with hardly any annotated data. We show how bootstrapping from seed examples or patterns scales up to very large text collections on the Web. We also present machine learning techniques in which data redundancy and variability lead to fast and reliable relation extraction., Discusses the recognition in text of semantic relations which capture interactions between base noun phrases. After a brief historical background, this text introduces a range of relation inventories of varying granularity, which have been proposed by computational linguists., People make sense of a text by identifying the semantic relations which connect the entities or concepts described by that text. A system which aspires to human-like performance must also be equipped to identify, and learn from, semantic relations in the texts it processes. Understanding even a simple sentence such as Opportunity and Curiosity find similar rocks on Mars requires recognizing relations (rocks are located on Mars, signalled by the word on) and drawing on already known relations (Opportunity and Curiosity are instances of the class of Mars rovers). A language-understanding system should be able to find such relations in documents and progressively build a knowledge base or even an ontology. Resources of this kind assist continuous learning and other advanced language-processing tasks such as text summarization, question answering and machine translation. The book discusses the recognition in text of semantic relations which capture interactions between base noun phrases. After a brief historical background, we introduce a range of relation inventories of varying granularity, which have been proposed by computational linguists. There is also variation in the scale at which systems operate, from snippets all the way to the whole Web, and in the techniques of recognizing relations in texts, from full supervision through weak or distant supervision to self-supervised or completely unsupervised methods. A discussion of supervised learning covers available datasets, feature sets which describe relation instances, and successful algorithms. An overview of weakly supervised and unsupervised learning zooms in on the acquisition of relations from large corpora with hardly any annotated data. We show how bootstrapping from seed examples or patterns scales up to very large text collections on the Web. We also present machine learning techniques in which data redundancy and variability lead to fast and reliable relation extraction., People make sense of a text by identifying the semantic relations which connect the entities or concepts described by that text. A system which aspires to human-like performance must also be equipped to identify, and learn from, semantic relations in the texts it processes. Understanding even a simple sentence such as ""Opportunity and Curiosity find similar rocks on Mars"" requires recognizing relations (rocks are located on Mars, signalled by the word on) and drawing on already known relations (Opportunity and Curiosity are instances of the class of Mars rovers). A language-understanding system should be able to find such relations in documents and progressively build a knowledge base or even an ontology. Resources of this kind assist continuous learning and other advanced language-processing tasks such as text summarization, question answering and machine translation. The book discusses the recognition in text of semantic relations which capture interactions between base noun phrases. After a brief historical background, we introduce a range of relation inventories of varying granularity, which have been proposed by computational linguists. There is also variation in the scale at which systems operate, from snippets all the way to the whole Web, and in the techniques of recognizing relations in texts, from full supervision through weak or distant supervision to self-supervised or completely unsupervised methods. A discussion of supervised learning covers available datasets, feature sets which describe relation instances, and successful algorithms. An overview of weakly supervised and unsupervised learning zooms in on the acquisition of relations from large corpora with hardly any annotated data. We show how bootstrapping from seed examples or patterns scales up to very large text collections on the Web. We also present machine learning techniques in which data redundancy and variability lead to fast and reliable relation extraction.
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