Difference between revisions of "Automatic Keyphrase Annotation of Scientific Documents Using Wikipedia and Genetic Algorithms"
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| + | | title = Automatic Keyphrase Annotation of Scientific Documents Using Wikipedia and Genetic Algorithms | ||
| + | | date = 2013 | ||
| + | | authors = [[Arash Joorabchi]]<br />[[Abdulhussain E. Mahdi]] | ||
| + | | doi = 10.1177/0165551512472138 | ||
| + | | link = http://dl.acm.org/citation.cfm?id=2493909.2493911 | ||
| + | }} | ||
'''Automatic Keyphrase Annotation of Scientific Documents Using Wikipedia and Genetic Algorithms''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Arash Joorabchi]] and [[Abdulhussain E. Mahdi]]. | '''Automatic Keyphrase Annotation of Scientific Documents Using Wikipedia and Genetic Algorithms''' - scientific work related to [[Wikipedia quality]] published in 2013, 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 to both human readers and [[information retrieval]] systems. This article describes a machine learning-based keyphrase annotation method for scientific documents that utilizes [[Wikipedia]] as a thesaurus for candidate selection from documents' content. Authors have devised a set of 20 statistical, positional and semantical [[features]] for candidate phrases to capture and reflect various properties of those candidates that have the highest keyphraseness probability. Authors first introduce a simple unsupervised method for ranking and filtering the most probable keyphrases, and then evolve it into a novel supervised method using genetic algorithms. Authors have evaluated the performance of both methods on a third-party dataset of research papers. Reported experimental results show that the performance of proposed methods, measured in terms of consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised and unsupervised methods. | Topical annotation of documents with keyphrases is a proven method for revealing the subject of scientific and research documents to both human readers and [[information retrieval]] systems. This article describes a machine learning-based keyphrase annotation method for scientific documents that utilizes [[Wikipedia]] as a thesaurus for candidate selection from documents' content. Authors have devised a set of 20 statistical, positional and semantical [[features]] for candidate phrases to capture and reflect various properties of those candidates that have the highest keyphraseness probability. Authors first introduce a simple unsupervised method for ranking and filtering the most probable keyphrases, and then evolve it into a novel supervised method using genetic algorithms. Authors have evaluated the performance of both methods on a third-party dataset of research papers. Reported experimental results show that the performance of proposed methods, measured in terms of consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised and unsupervised methods. | ||
Revision as of 15:12, 22 March 2021
Authors | Arash Joorabchi Abdulhussain E. Mahdi |
|---|---|
Publication date | 2013 |
DOI | 10.1177/0165551512472138 |
Links | Original |
Automatic Keyphrase Annotation of Scientific Documents Using Wikipedia and Genetic Algorithms - scientific work related to Wikipedia quality published in 2013, 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 to both human readers and information retrieval systems. This article describes a machine learning-based keyphrase annotation method for scientific documents that utilizes Wikipedia as a thesaurus for candidate selection from documents' content. Authors have devised a set of 20 statistical, positional and semantical features for candidate phrases to capture and reflect various properties of those candidates that have the highest keyphraseness probability. Authors first introduce a simple unsupervised method for ranking and filtering the most probable keyphrases, and then evolve it into a novel supervised method using genetic algorithms. Authors have evaluated the performance of both methods on a third-party dataset of research papers. Reported experimental results show that the performance of proposed methods, measured in terms of consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised and unsupervised methods.
