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  1. Transactions on Speech and Language Processing (TSLP)
  2. ACM Transactions on Speech and Language Processing (TSLP) : Volume 10
  3. Issue 2(Special issue on multiword expressions: From theory to practice and use, part 1), June 2013
  4. Learning to detect english and hungarian light verb constructions
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ACM Transactions on Speech and Language Processing (TSLP) : Volume 10
Issue 4, December 2013
Issue 3(Special issue on multiword expressions: From theory to practice and use, part 2), July 2013
Issue 2(Special issue on multiword expressions: From theory to practice and use, part 1), June 2013
Introduction to the special issue on multiword expressions: From theory to practice and use
How many multiword expressions do people know?
Lexical semantic factors in the acceptability of english support-verb-nominalization constructions
Learning to detect english and hungarian light verb constructions
Modeling the internal variability of multiword expressions through a pattern-based method
Reviewers
Issue 1, March 2013
ACM Transactions on Speech and Language Processing (TSLP) : Volume 9
ACM Transactions on Speech and Language Processing (TSLP) : Volume 8
ACM Transactions on Speech and Language Processing (TSLP) : Volume 7
ACM Transactions on Speech and Language Processing (TSLP) : Volume 6
ACM Transactions on Speech and Language Processing (TSLP) : Volume 5
ACM Transactions on Speech and Language Processing (TSLP) : Volume 4
ACM Transactions on Speech and Language Processing (TSLP) : Volume 3
ACM Transactions on Speech and Language Processing (TSLP) : Volume 2
ACM Transactions on Speech and Language Processing (TSLP) : Volume 1

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Learning to detect english and hungarian light verb constructions

Content Provider ACM Digital Library
Author Zsibrita, János Nagy t., István Vincze, Veronika
Copyright Year 2013
Abstract Light verb constructions consist of a verbal and a nominal component, where the noun preserves its original meaning while the verb has lost it (to some degree). They are syntactically flexible and their meaning can only be partially computed on the basis of the meaning of their parts, thus they require special treatment in natural language processing. For this purpose, the first step is to identify light verb constructions. In this study, we present our conditional random fields-based tool—called FXTagger—for identifying light verb constructions. The flexibility of the tool is demonstrated on two, typologically different, languages, namely, English and Hungarian. As earlier studies labeled different linguistic phenomena as light verb constructions, we first present a linguistics-based classification of light verb constructions and then show that FXTagger is able to identify different classes of light verb constructions in both languages. Different types of texts may contain different types of light verb constructions; moreover, the frequency of light verb constructions may differ from domain to domain. Hence we focus on the portability of models trained on different corpora, and we also investigate the effect of simple domain adaptation techniques to reduce the gap between the domains. Our results show that in spite of domain specificities, out-domain data can also contribute to the successful LVC detection in all domains.
Starting Page 1
Ending Page 25
Page Count 25
File Format PDF
ISSN 15504875
e-ISSN 15504883
DOI 10.1145/2483691.2483695
Volume Number 10
Issue Number 2
Journal ACM Transactions on Speech and Language Processing (TSLP)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-06-21
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Conditional random fields English Hungarian Corpora Domain adaptation Light verb constructions Multiword expressions
Content Type Text
Resource Type Article
Subject 1700/1701 Computational Mathematics
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