POS-ATAEPE-BiLSTM: an aspect-based sentiment analysis algorithm considering part-of-speech embedding.
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| Title: | POS-ATAEPE-BiLSTM: an aspect-based sentiment analysis algorithm considering part-of-speech embedding. |
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| Authors: | Zhao, Qizhi1, 515686861@qq.com, Mo, Zan1, Fan, Mengting1 |
| Source: | Applied Intelligence; Nov2023, Vol. 53 Issue 22, p27440-27458, 19p |
| Database: | Applied Science & Technology Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 173178634 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: POS-ATAEPE-BiLSTM: an aspect-based sentiment analysis algorithm considering part-of-speech embedding. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Zhao%2C+Qizhi%22">Zhao, Qizhi</searchLink><relatesTo>1</relatesTo>, <i>515686861@qq.com</i><br /><searchLink fieldCode="AU" term="%22Mo%2C+Zan%22">Mo, Zan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Fan%2C+Mengting%22">Fan, Mengting</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>; Nov2023, Vol. 53 Issue 22, p27440-27458, 19p |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=173178634 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10489-023-04952-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 27440 Titles: – TitleFull: POS-ATAEPE-BiLSTM: an aspect-based sentiment analysis algorithm considering part-of-speech embedding. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, Qizhi – PersonEntity: Name: NameFull: Mo, Zan – PersonEntity: Name: NameFull: Fan, Mengting IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 11 Text: Nov2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 53 – Type: issue Value: 22 Titles: – TitleFull: Applied Intelligence Type: main |
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