Livre: Nastase et al, Semantic Relations Between Nominals

[Thierry Hamon]

Date: Wed, 26 Jun 2013 12:11:25 -0400
From: Graeme Hirst <gh at cs.toronto.edu>
Message-Id: <6194FBE2-322E-4048-9084-9DEFAFC6C3C6 at cs.toronto.edu>
X-url: http://www.morganclaypool.com/doi/abs/10.2200/S00489ED1V01Y201303HLT019


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


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