Composition pattern oriented tag extraction from short documents using a structural learning method.
Saved in:
| Title: | Composition pattern oriented tag extraction from short documents using a structural learning method. |
|---|---|
| Authors: | Shin, Yongwook1, Lee, Sung-Jun1, Park, Jonghun1 jonghun@snu.ac.kr |
| Source: | Knowledge & Information Systems. Feb2014, Vol. 38 Issue 2, p447-468. 22p. |
| Subjects: | Data mining, Structural learning theory, Information sharing, Support vector machines, Information retrieval, Social media |
| Abstract: | With the rapid growth of web, automatic tagging that detects informative terms from a document becomes an important problem for information aggregation and sharing services. In particular, automatic tagging for short documents becomes more interesting as many users are increasingly publishing information through social media services which encourage users to create the documents of short length. In this paper, we propose a novel automatic tagging model for short text documents from social media services, following the framework of supervised learning. We redefine traditional frequency-based term features so that they can address the properties of the documents created under length limitation and consider sequential dependencies between successive terms in a document based on a structural support vector machine. In addition, our proposed approach incorporates composition patterns by which users put informative terms into their documents. Extensive experiments have been conducted to validate the presented approach, and it was found that the proposed term features were effective for extracting tags, and the tag extractor trained by considering the sequential dependencies and composition patterns achieved superior performance results over the existing alternative methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Knowledge & Information Systems is the property of Springer Nature 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: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 93752498 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Composition pattern oriented tag extraction from short documents using a structural learning method. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shin%2C+Yongwook%22">Shin, Yongwook</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lee%2C+Sung-Jun%22">Lee, Sung-Jun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Park%2C+Jonghun%22">Park, Jonghun</searchLink><relatesTo>1</relatesTo><i> jonghun@snu.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Knowledge+%26+Information+Systems%22">Knowledge & Information Systems</searchLink>. Feb2014, Vol. 38 Issue 2, p447-468. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+learning+theory%22">Structural learning theory</searchLink><br /><searchLink fieldCode="DE" term="%22Information+sharing%22">Information sharing</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Social+media%22">Social media</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the rapid growth of web, automatic tagging that detects informative terms from a document becomes an important problem for information aggregation and sharing services. In particular, automatic tagging for short documents becomes more interesting as many users are increasingly publishing information through social media services which encourage users to create the documents of short length. In this paper, we propose a novel automatic tagging model for short text documents from social media services, following the framework of supervised learning. We redefine traditional frequency-based term features so that they can address the properties of the documents created under length limitation and consider sequential dependencies between successive terms in a document based on a structural support vector machine. In addition, our proposed approach incorporates composition patterns by which users put informative terms into their documents. Extensive experiments have been conducted to validate the presented approach, and it was found that the proposed term features were effective for extracting tags, and the tag extractor trained by considering the sequential dependencies and composition patterns achieved superior performance results over the existing alternative methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Knowledge & Information Systems is the property of Springer Nature 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=egs&AN=93752498 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10115-012-0594-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 447 Subjects: – SubjectFull: Data mining Type: general – SubjectFull: Structural learning theory Type: general – SubjectFull: Information sharing Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Information retrieval Type: general – SubjectFull: Social media Type: general Titles: – TitleFull: Composition pattern oriented tag extraction from short documents using a structural learning method. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shin, Yongwook – PersonEntity: Name: NameFull: Lee, Sung-Jun – PersonEntity: Name: NameFull: Park, Jonghun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 02191377 Numbering: – Type: volume Value: 38 – Type: issue Value: 2 Titles: – TitleFull: Knowledge & Information Systems Type: main |
| ResultId | 1 |
