Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/80145
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dc.contributor.authorKathleen Swee, Neo Tan-
dc.contributor.authorTong, Ming Lim-
dc.contributor.authorChi, Wee Tan-
dc.contributor.authorWei, Wei Chew-
dc.date.accessioned2024-02-27T06:43:55Z-
dc.date.available2024-02-27T06:43:55Z-
dc.date.issued2022-
dc.identifier.citationThe Journal of the Institution of Engineers, Malaysia, Special Ed., 2022, pages 95-100.en_US
dc.identifier.issn0126-513x-
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/80145-
dc.descriptionLink to publisher’s home pages at https://www.myiem.org.my/en_US
dc.description.abstractLight verb constructions (LVC) are complex predicates that are present in many languages. They belong to the Multiword Expression (MWE) category known as verbal MWEs and has the canonical form of verb+noun. Examples of LVCs include give help, make decisions, and take walks. LVC identification is essential for many natural processing (NLP) applications such as machine translation, sentiment analysis, and information extraction. However, the task of LVC identification is challenging due to its characteristics such as variability, discontinuity, and ambiguity. This paper presents a review of recent work, discusses the gaps that still exist, and proposes some future work that may contribute significant progress in LVC identification.en_US
dc.language.isoenen_US
dc.publisherThe Institution of Engineers, Malaysia (IEM)en_US
dc.subject.otherLight verb constructionsen_US
dc.subject.otherMultiword expressionsen_US
dc.subject.otherComputational linguisticsen_US
dc.subject.otherNatural language processingen_US
dc.titleAutomatic identification of light verb constructions: a reviewen_US
dc.typeArticleen_US
dc.identifier.urlhttps://www.myiem.org.my/-
dc.contributor.urltansn@tarc.edu.myen_US
Appears in Collections:IEM Journal

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