Optimizing Selection of Assessment Solutions for Completing Information Extraction Results
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Redalyc
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| Formato: | Artículo científico |
| Lenguaje: | en |
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Instituto Politécnico Nacional
2013
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| _version_ | 1876433339013922816 |
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| author | Christina Feilmayr |
| author_facet | Christina Feilmayr |
| contents | Optimizing Selection of Assessment Solutions for Completing Information Extraction Results Christina Feilmayr Computación method selection information quality data and text mining Information extraction Incomplete information produces serious consequences in information extraction: it increases costs and leads to problems in downstream processing. This work focuses on improving the completeness of extraction results by applying judiciously selected assessment methods to information extraction based on the principle of complementarity. Our recommendation model simplifies the selection of assessment methods which can overcome a specific incompleteness problem. This paper also focuses on the characterization of information extraction and assessment methods as well as on a rule-based approach that allows estimation of general processability, profitability in the complementarity approach, and the performance of an assessment method under evaluation. 2013 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61527437007 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.2 Vol.17 |
| format | Artículo científico |
| id | redalyc_61527437007 |
| institution | Redalyc |
| language | en |
| publishDate | 2013 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Optimizing Selection of Assessment Solutions for Completing Information Extraction Results Christina Feilmayr Computación method selection information quality data and text mining Information extraction Optimizing Selection of Assessment Solutions for Completing Information Extraction Results Christina Feilmayr Computación method selection information quality data and text mining Information extraction Incomplete information produces serious consequences in information extraction: it increases costs and leads to problems in downstream processing. This work focuses on improving the completeness of extraction results by applying judiciously selected assessment methods to information extraction based on the principle of complementarity. Our recommendation model simplifies the selection of assessment methods which can overcome a specific incompleteness problem. This paper also focuses on the characterization of information extraction and assessment methods as well as on a rule-based approach that allows estimation of general processability, profitability in the complementarity approach, and the performance of an assessment method under evaluation. 2013 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61527437007 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.2 Vol.17 |
| title | Optimizing Selection of Assessment Solutions for Completing Information Extraction Results |
| topic | Computación method selection information quality data and text mining Information extraction |
| url | https://www.redalyc.org/articulo.oa?id=61527437007 |