Concept Representation by Learning Explicit and Implicit Concept Couplings

Lu, W; Zhang, Y; Wang, S; Huang, H; Liu, Q; Luo, S

Concept Representation by Learning Explicit and Implicit Concept Couplings

Lu, W Zhang, Y Wang, S

Huang, H Liu, Q Luo, S

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Intelligent Systems, 2021, 36, (1), pp. 6-15
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Lu, W
dc.contributor.author	Zhang, Y
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.contributor.author	Huang, H
dc.contributor.author	Liu, Q
dc.contributor.author	Luo, S
dc.date.accessioned	2022-04-10T01:06:08Z
dc.date.available	2022-04-10T01:06:08Z
dc.date.issued	2021
dc.identifier.citation	IEEE Intelligent Systems, 2021, 36, (1), pp. 6-15
dc.identifier.issn	1541-1672
dc.identifier.issn	1941-1294
dc.identifier.uri	http://hdl.handle.net/10453/156006
dc.description.abstract	IEEE Generating the precise semantic representation of a word/concept is a fundamental task in natural language processing. Recent studies which incorporate semantic knowledge into word embedding have shown their potential in improving the semantic representation of a concept. However, existing approaches only achieved limited performance improvement as they usually (1) model a word's semantics from some explicit aspects while ignoring the intrinsic aspects of the word, (2) treat semantic knowledge as a supplement of word embeddings, and (3) consider partial relations between concepts while ignoring rich coupling relations between them, such as explicit concept co-occurrences in descriptive texts in a corpus as well as concept hyperlink relations in a knowledge network, and implicit couplings between the explicit relations. In human consciousness, concepts are associated with various coupling relations, which inspires us to capture as many concept couplings as possible for building a better concept representation. We thus propose a neural coupled concept representation (CoupledCR) framework and its instantiation: a coupled concept embedding (CCE) model. CCE first learns two types of explicit couplings from concept cooccurrences and hyperlink relations respectively, and then learns a type of high-level implicit couplings between these two types of explicit couplings. Extensive experimental results on real-world datasets show that CCE significantly outperforms state-of-the-art semantic representation methods.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Intelligent Systems
dc.relation.isbasedon	10.1109/MIS.2020.3021188
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Concept Representation by Learning Explicit and Implicit Concept Couplings
dc.type	Journal Article
utslib.citation.volume	36
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-04-10T01:06:06Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	36
utslib.citation.issue	1

Abstract:

IEEE Generating the precise semantic representation of a word/concept is a fundamental task in natural language processing. Recent studies which incorporate semantic knowledge into word embedding have shown their potential in improving the semantic representation of a concept. However, existing approaches only achieved limited performance improvement as they usually (1) model a word's semantics from some explicit aspects while ignoring the intrinsic aspects of the word, (2) treat semantic knowledge as a supplement of word embeddings, and (3) consider partial relations between concepts while ignoring rich coupling relations between them, such as explicit concept co-occurrences in descriptive texts in a corpus as well as concept hyperlink relations in a knowledge network, and implicit couplings between the explicit relations. In human consciousness, concepts are associated with various coupling relations, which inspires us to capture as many concept couplings as possible for building a better concept representation. We thus propose a neural coupled concept representation (CoupledCR) framework and its instantiation: a coupled concept embedding (CCE) model. CCE first learns two types of explicit couplings from concept cooccurrences and hyperlink relations respectively, and then learns a type of high-level implicit couplings between these two types of explicit couplings. Extensive experimental results on real-world datasets show that CCE significantly outperforms state-of-the-art semantic representation methods.

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http://hdl.handle.net/10453/156006