Syntactic systematicity arising from semantic predictions in a Hebbian-competitive network.
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| Title: | Syntactic systematicity arising from semantic predictions in a Hebbian-competitive network. |
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| Authors: | Hadley, Robert F., Rotaru-Varga, Adam, Arnold, Dirk V., Cardei, Vlad C. |
| Source: | Connection Science. Mar2001, Vol. 13 Issue 1, p73-94. 22p. 1 Diagram, 1 Chart, 8 Graphs. |
| Subjects: | Semantics, Learning |
| Abstract: | A Hebbian-inspired, competitive network is presented which learns to predict the typical semantic features of denoting terms in simple and moderately complex sentences. In addition, the network learns to predict the appearance of syntactically key words, such as prepositions and relative pronouns. Importantly, as a by-product of the network's semantic training, a strong form of syntactic systematicity emerges. This systematicity is exhibited even at a novel, deeper level of clausal embedding. All network training is unsupervised with respect to error feedback. A novel variant of competitive learning and an unusual hierarchical architecture are presented. The relationship of this work to issues raised by Marcus and Phillips is explored. [ABSTRACT FROM AUTHOR] |
| Copyright of Connection Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 5393136 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Syntactic systematicity arising from semantic predictions in a Hebbian-competitive network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hadley%2C+Robert+F%2E%22">Hadley, Robert F.</searchLink><br /><searchLink fieldCode="AR" term="%22Rotaru-Varga%2C+Adam%22">Rotaru-Varga, Adam</searchLink><br /><searchLink fieldCode="AR" term="%22Arnold%2C+Dirk+V%2E%22">Arnold, Dirk V.</searchLink><br /><searchLink fieldCode="AR" term="%22Cardei%2C+Vlad+C%2E%22">Cardei, Vlad C.</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Connection+Science%22">Connection Science</searchLink>. Mar2001, Vol. 13 Issue 1, p73-94. 22p. 1 Diagram, 1 Chart, 8 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A Hebbian-inspired, competitive network is presented which learns to predict the typical semantic features of denoting terms in simple and moderately complex sentences. In addition, the network learns to predict the appearance of syntactically key words, such as prepositions and relative pronouns. Importantly, as a by-product of the network's semantic training, a strong form of syntactic systematicity emerges. This systematicity is exhibited even at a novel, deeper level of clausal embedding. All network training is unsupervised with respect to error feedback. A novel variant of competitive learning and an unusual hierarchical architecture are presented. The relationship of this work to issues raised by Marcus and Phillips is explored. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Connection Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=5393136 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/09540090110052996 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 73 Subjects: – SubjectFull: Semantics Type: general – SubjectFull: Learning Type: general Titles: – TitleFull: Syntactic systematicity arising from semantic predictions in a Hebbian-competitive network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hadley, Robert F. – PersonEntity: Name: NameFull: Rotaru-Varga, Adam – PersonEntity: Name: NameFull: Arnold, Dirk V. – PersonEntity: Name: NameFull: Cardei, Vlad C. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2001 Type: published Y: 2001 Identifiers: – Type: issn-print Value: 09540091 Numbering: – Type: volume Value: 13 – Type: issue Value: 1 Titles: – TitleFull: Connection Science Type: main |
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