Difference between revisions of "Automatic Keyphrase Annotation of Scientific Documents Using Wikipedia and Genetic Algorithms"
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== 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. | ||
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| + | == Embed == | ||
| + | === Wikipedia Quality === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Joorabchi, Arash; Mahdi, Abdulhussain E.. (2013). "[[Automatic Keyphrase Annotation of Scientific Documents Using Wikipedia and Genetic Algorithms]]". Sage Publications. DOI: 10.1177/0165551512472138. | ||
| + | </nowiki> | ||
| + | </code> | ||
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| + | === English Wikipedia === | ||
| + | <code> | ||
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| + | {{cite journal |last1=Joorabchi |first1=Arash |last2=Mahdi |first2=Abdulhussain E. |title=Automatic Keyphrase Annotation of Scientific Documents Using Wikipedia and Genetic Algorithms |date=2013 |doi=10.1177/0165551512472138 |url=https://wikipediaquality.com/wiki/Automatic_Keyphrase_Annotation_of_Scientific_Documents_Using_Wikipedia_and_Genetic_Algorithms |journal=Sage Publications}} | ||
| + | </nowiki> | ||
| + | </code> | ||
| + | |||
| + | === HTML === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Joorabchi, Arash; Mahdi, Abdulhussain E.. (2013). &quot;<a href="https://wikipediaquality.com/wiki/Automatic_Keyphrase_Annotation_of_Scientific_Documents_Using_Wikipedia_and_Genetic_Algorithms">Automatic Keyphrase Annotation of Scientific Documents Using Wikipedia and Genetic Algorithms</a>&quot;. Sage Publications. DOI: 10.1177/0165551512472138. | ||
| + | </nowiki> | ||
| + | </code> | ||
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| + | [[Category:Scientific works]] | ||
Latest revision as of 07:23, 23 April 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.
Embed
Wikipedia Quality
Joorabchi, Arash; Mahdi, Abdulhussain E.. (2013). "[[Automatic Keyphrase Annotation of Scientific Documents Using Wikipedia and Genetic Algorithms]]". Sage Publications. DOI: 10.1177/0165551512472138.
English Wikipedia
{{cite journal |last1=Joorabchi |first1=Arash |last2=Mahdi |first2=Abdulhussain E. |title=Automatic Keyphrase Annotation of Scientific Documents Using Wikipedia and Genetic Algorithms |date=2013 |doi=10.1177/0165551512472138 |url=https://wikipediaquality.com/wiki/Automatic_Keyphrase_Annotation_of_Scientific_Documents_Using_Wikipedia_and_Genetic_Algorithms |journal=Sage Publications}}
HTML
Joorabchi, Arash; Mahdi, Abdulhussain E.. (2013). "<a href="https://wikipediaquality.com/wiki/Automatic_Keyphrase_Annotation_of_Scientific_Documents_Using_Wikipedia_and_Genetic_Algorithms">Automatic Keyphrase Annotation of Scientific Documents Using Wikipedia and Genetic Algorithms</a>". Sage Publications. DOI: 10.1177/0165551512472138.
