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Author: Vivi Nastase
Publisher: Springer Nature
ISBN: 3031021789
Category : Computers
Languages : en
Pages : 220
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Book Description
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.
Author: Vivi Nastase
Publisher: Springer Nature
ISBN: 3031021789
Category : Computers
Languages : en
Pages : 220
Get Book Here
Book Description
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.
Author: Vivi Nastase
Publisher: Morgan & Claypool Publishers
ISBN: 1636390870
Category : Computers
Languages : en
Pages : 236
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Book Description
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.
Author: Vivi Nastase
Publisher: Springer Nature
ISBN: 3031021487
Category : Computers
Languages : en
Pages : 116
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Book Description
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.
Author: Vivi Nastase
Publisher: Morgan & Claypool Publishers
ISBN: 9781608459803
Category : Computers
Languages : en
Pages : 119
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Book Description
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.
Author: Martin Schäfer
Publisher: Language Science Press
ISBN: 3961100306
Category :
Languages : en
Pages : 420
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Book Description
What is semantic transparency, why is it important, and which factors play a role in its assessment? This work approaches these questions by investigating English compound nouns. The first part of the book gives an overview of semantic transparency in the analysis of compound nouns, discussing its role in models of morphological processing and differentiating it from related notions. After a chapter on the semantic analysis of complex nominals, it closes with a chapter on previous attempts to model semantic transparency. The second part introduces new empirical work on semantic transparency, introducing two different sets of statistical models for compound transparency. In particular, two semantic factors were explored: the semantic relations holding between compound constituents and the role of different readings of the constituents and the whole compound, operationalized in terms of meaning shifts and in terms of the distribution of specifc readings across constituent families. All semantic annotations used in the book are freely available.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Alain Auger
Publisher: John Benjamins Publishing
ISBN: 9027287929
Category : Language Arts & Disciplines
Languages : en
Pages : 168
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Book Description
Semantic relations are at the core of any representational system, and are keys to enable the next generation of information processing systems with semantic and reasoning capabilities. Acquisition, description, and formalization of semantic relations are fundamentals in computer-based systems where natural language processing is required. Probing Semantic Relations provides a state of the art of current research trends in the area of knowledge extraction from text using linguistic patterns. First published as a Special Issue of Terminology 14:1 (2008), the current book emphasizes how definitional knowledge is conveyed by conceptual and semantic relations such as synonymy, causality, hypernymy (generic–specific), and meronymy (part–whole). Showing the difficulties and successes of pattern-based approaches, the book illustrates current and future challenges in knowledge acquisition from text. This book provides new perspectives to researchers and practitioners in terminology, knowledge engineering, natural language processing, and semantics.
Author: Zahir Tari
Publisher: Springer
ISBN: 3540768904
Category : Computers
Languages : en
Pages : 610
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Book Description
This two-volume set LNCS 4805/4806 constitutes the refereed proceedings of 10 international workshops and papers of the OTM Academy Doctoral Consortium held as part of OTM 2007 in Vilamoura, Portugal, in November 2007. The 126 revised full papers presented were carefully reviewed and selected from a total of 241 submissions to the workshops. The first volume begins with 23 additional revised short or poster papers of the OTM 2007 main conferences.
Author: M. Lynne Murphy
Publisher: Cambridge University Press
ISBN: 1139437453
Category : Language Arts & Disciplines
Languages : en
Pages : 306
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Book Description
Semantic Relations and the Lexicon explores the many paradigmatic semantic relations between words, such as synonymy, antonymy and hyponymy, and their relevance to the mental organization of our vocabularies. Drawing on a century's research in linguistics, psychology, philosophy, anthropology and computer science, M. Lynne Murphy proposes a pragmatic approach to these relations. Whereas traditional approaches have claimed that paradigmatic relations are part of our lexical knowledge, Dr Murphy argues that they constitute metalinguistic knowledge, which can be derived through a single relational principle, and may also be stored as part of our extra-lexical, conceptual representations of a word. Part I shows how this approach can account for the properties of lexical relations in ways that traditional approaches cannot, and Part II examines particular relations in detail. This book will serve as an informative handbook for all linguists and cognitive scientists interested in the mental representation of vocabulary.
Author: Ruth Singer
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 1501501208
Category : Language Arts & Disciplines
Languages : en
Pages : 262
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Book Description
The use of grammatical gender in the Australian language Mawng calls into question prevailing ideas about the functions of nominal classification systems. Mawng’s gender system has a strong semantic basis and plays an important role in the construction of meaning in discourse. Gender agreement in verbs is frequently lexicalized, creating idioms called lexicalised agreement verbs that are structurally similar to noun-verb idioms. This book will be of interest to anyone interested in nominal classification or cross-linguistic approaches to idioms.
