32.1356, Books: Semantic Relations Between Nominals, Second Edition: Nastase, Szpakowicz, Nakov, Ó Séagdha

[The LINGUIST List]

LINGUIST List: Vol-32-1356. Fri Apr 16 2021. ISSN: 1069 - 4875.

Subject: 32.1356, Books: Semantic Relations Between Nominals, Second Edition: Nastase, Szpakowicz, Nakov, Ó Séagdha

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Date: Fri, 16 Apr 2021 17:29:23
From: Brent Beckley [beckley at morganclaypool.com]
Subject: Semantic Relations Between Nominals, Second Edition: Nastase, Szpakowicz, Nakov, Ó Séagdha

 


Title: Semantic Relations Between Nominals, Second Edition 
Series Title: Human Language Technologies  

Publication Year: 2021 
Publisher: Morgan & Claypool Publishers
	   http://www.morganclaypool.com
	

Book URL: http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1627 


Author: Vivi Nastase
Author: Stan Szpakowicz
Author: Preslav Nakov
Author: Diarmuid Ó Séagdha

Electronic: ISBN:  9781636390871 Pages: 234 Price: U.S. $ 71.96
Hardback: ISBN:  9781636390888 Pages: 234 Price: U.S. $ 109.95
Paperback: ISBN:  9781636390864 Pages: 234 Price: U.S. $ 89.95


Abstract:

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.
 



Linguistic Field(s): Semantics


Written In: English  (eng)

See this book announcement on our website: 
http://linguistlist.org/pubs/books/get-book.cfm?BookID=153633




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