Difference between revisions of "Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms"
(Adding wikilinks) |
(Infobox) |
||
| Line 1: | Line 1: | ||
| + | {{Infobox work | ||
| + | | title = Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms | ||
| + | | date = 2012 | ||
| + | | authors = [[Arash Joorabchi]]<br />[[Abdulhussain E. Mahdi]] | ||
| + | | doi = 10.1007/978-3-642-33876-2_6 | ||
| + | | link = https://dl.acm.org/citation.cfm?id=2413951 | ||
| + | }} | ||
'''Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Arash Joorabchi]] and [[Abdulhussain E. Mahdi]]. | '''Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Arash Joorabchi]] and [[Abdulhussain E. Mahdi]]. | ||
== Overview == | == Overview == | ||
Topical annotation of documents with keyphrases is a proven method for revealing the subject of scientific and research documents. However, scientific documents that are manually annotated with keyphrases are in the minority. This paper describes a machine learning-based automatic keyphrase annotation method for scientific documents, which utilizes [[Wikipedia]] as a thesaurus for candidate selection from documents' content and deploys genetic algorithms to learn a model for ranking and filtering the most probable keyphrases. Reported experimental results show that the performance of method, evaluated in terms of inter-consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised methods. | Topical annotation of documents with keyphrases is a proven method for revealing the subject of scientific and research documents. However, scientific documents that are manually annotated with keyphrases are in the minority. This paper describes a machine learning-based automatic keyphrase annotation method for scientific documents, which utilizes [[Wikipedia]] as a thesaurus for candidate selection from documents' content and deploys genetic algorithms to learn a model for ranking and filtering the most probable keyphrases. Reported experimental results show that the performance of method, evaluated in terms of inter-consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised methods. | ||
Revision as of 08:34, 10 October 2019
Authors | Arash Joorabchi Abdulhussain E. Mahdi |
|---|---|
Publication date | 2012 |
DOI | 10.1007/978-3-642-33876-2_6 |
Links | Original |
Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms - scientific work related to Wikipedia quality published in 2012, written by Arash Joorabchi and Abdulhussain E. Mahdi.
Overview
Topical annotation of documents with keyphrases is a proven method for revealing the subject of scientific and research documents. However, scientific documents that are manually annotated with keyphrases are in the minority. This paper describes a machine learning-based automatic keyphrase annotation method for scientific documents, which utilizes Wikipedia as a thesaurus for candidate selection from documents' content and deploys genetic algorithms to learn a model for ranking and filtering the most probable keyphrases. Reported experimental results show that the performance of method, evaluated in terms of inter-consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised methods.
