[Corpora-List] New Book: Nastase et al: Semantic Relations Between Nominals

[Graeme Hirst]

BOOK ANNOUNCEMENT

Semantic Relations Between Nominals

by
Vivi Nastase, FBK, Trento, Italy 
Preslav Nakov, QCRI, Qatar Foundation
Diarmuid Ó Séaghdha, Computer Laboratory, University of Cambridge, UK
Stan Szpakowicz, EECS, University of Ottawa and ICS, Polish Academy of Sciences

Synthesis Lectures on Human Language Technologies #19 (Morgan & Claypool Publishers), 2013, 119 pages

Abstract

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.

Table of Contents: Introduction / Relations between Nominals, Relations between Concepts / Extracting Semantic Relations with Supervision / Extracting Semantic Relations with Little or No Supervision / Conclusion

http://www.morganclaypool.com/doi/abs/10.2200/S00489ED1V01Y201303HLT019


This title is available online without charge to members of institutions that have licensed the Synthesis Digital Library of Engineering and Computer Science.  Members of licensing institutions have unlimited access to download, save, and print the PDF without restriction; use of the book as a course text is encouraged.  To find out whether your institution is a subscriber, visit http://www.morganclaypool.com/page/licensed, or just click on the book's URL above from an institutional IP address and attempt to download the PDF.  Others may purchase the book from this URL as a PDF download for US$30 or in print for US$40.  Printed copies are also available from Amazon and from booksellers worldwide at approximately US$45 or local currency equivalent. 


--
::::  Graeme Hirst * Series editor, Synthesis Lectures in Human Language Technologies
::::  University of Toronto * Department of Computer Science




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