Automatic Semantic Subject Indexing of Web Documents in Highly Inflected Languages
Identifieur interne : 001133 ( Istex/Corpus ); précédent : 001132; suivant : 001134Automatic Semantic Subject Indexing of Web Documents in Highly Inflected Languages
Auteurs : Reetta Sinkkil ; Osma Suominen ; Eero HyvönenSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: Structured semantic metadata about unstructured web documents can be created using automatic subject indexing methods, avoiding laborious manual indexing. A succesful automatic subject indexing tool for the web should work with texts in multiple languages and be independent of the domain of discourse of the documents and controlled vocabularies. However, analyzing text written in a highly inflected language requires word form normalization that goes beyond rule-based stemming algorithms. We have tested the state-of-the art automatic indexing tool Maui on Finnish texts using three stemming and lemmatization algorithms and tested it with documents and vocabularies of different domains. Both of the lemmatization algorithms we tested performed significantly better than a rule-based stemmer, and the subject indexing quality was found to be comparable to that of human indexers.
Url:
DOI: 10.1007/978-3-642-21034-1_15
Links to Exploration step
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<front><div type="abstract" xml:lang="en">Abstract: Structured semantic metadata about unstructured web documents can be created using automatic subject indexing methods, avoiding laborious manual indexing. A succesful automatic subject indexing tool for the web should work with texts in multiple languages and be independent of the domain of discourse of the documents and controlled vocabularies. However, analyzing text written in a highly inflected language requires word form normalization that goes beyond rule-based stemming algorithms. We have tested the state-of-the art automatic indexing tool Maui on Finnish texts using three stemming and lemmatization algorithms and tested it with documents and vocabularies of different domains. Both of the lemmatization algorithms we tested performed significantly better than a rule-based stemmer, and the subject indexing quality was found to be comparable to that of human indexers.</div>
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<abstract xml:lang="en"><p>Abstract: Structured semantic metadata about unstructured web documents can be created using automatic subject indexing methods, avoiding laborious manual indexing. A succesful automatic subject indexing tool for the web should work with texts in multiple languages and be independent of the domain of discourse of the documents and controlled vocabularies. However, analyzing text written in a highly inflected language requires word form normalization that goes beyond rule-based stemming algorithms. We have tested the state-of-the art automatic indexing tool Maui on Finnish texts using three stemming and lemmatization algorithms and tested it with documents and vocabularies of different domains. Both of the lemmatization algorithms we tested performed significantly better than a rule-based stemmer, and the subject indexing quality was found to be comparable to that of human indexers.</p>
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<Para>Structured semantic metadata about unstructured web documents can be created using automatic subject indexing methods, avoiding laborious manual indexing. A succesful automatic subject indexing tool for the web should work with texts in multiple languages and be independent of the domain of discourse of the documents and controlled vocabularies. However, analyzing text written in a highly inflected language requires word form normalization that goes beyond rule-based stemming algorithms. We have tested the state-of-the art automatic indexing tool Maui on Finnish texts using three stemming and lemmatization algorithms and tested it with documents and vocabularies of different domains. Both of the lemmatization algorithms we tested performed significantly better than a rule-based stemmer, and the subject indexing quality was found to be comparable to that of human indexers.</Para>
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<abstract lang="en">Abstract: Structured semantic metadata about unstructured web documents can be created using automatic subject indexing methods, avoiding laborious manual indexing. A succesful automatic subject indexing tool for the web should work with texts in multiple languages and be independent of the domain of discourse of the documents and controlled vocabularies. However, analyzing text written in a highly inflected language requires word form normalization that goes beyond rule-based stemming algorithms. We have tested the state-of-the art automatic indexing tool Maui on Finnish texts using three stemming and lemmatization algorithms and tested it with documents and vocabularies of different domains. Both of the lemmatization algorithms we tested performed significantly better than a rule-based stemmer, and the subject indexing quality was found to be comparable to that of human indexers.</abstract>
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<name type="personal"><namePart type="given">David</namePart>
<namePart type="family">Hutchison</namePart>
<affiliation>Lancaster University, Lancaster, UK</affiliation>
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<name type="personal"><namePart type="given">Takeo</namePart>
<namePart type="family">Kanade</namePart>
<affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation>
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<name type="personal"><namePart type="given">Josef</namePart>
<namePart type="family">Kittler</namePart>
<affiliation>University of Surrey, Guildford, UK</affiliation>
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<name type="personal"><namePart type="given">Jon</namePart>
<namePart type="given">M.</namePart>
<namePart type="family">Kleinberg</namePart>
<affiliation>Cornell University, Ithaca, NY, USA</affiliation>
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<name type="personal"><namePart type="given">Friedemann</namePart>
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<affiliation>ETH Zurich, Zurich, Switzerland</affiliation>
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<name type="personal"><namePart type="given">John</namePart>
<namePart type="given">C.</namePart>
<namePart type="family">Mitchell</namePart>
<affiliation>Stanford University, Stanford, CA, USA</affiliation>
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<name type="personal"><namePart type="given">Moni</namePart>
<namePart type="family">Naor</namePart>
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<name type="personal"><namePart type="given">Oscar</namePart>
<namePart type="family">Nierstrasz</namePart>
<affiliation>University of Bern, Bern, Switzerland</affiliation>
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<name type="personal"><namePart type="given">C.</namePart>
<namePart type="family">Pandu Rangan</namePart>
<affiliation>Indian Institute of Technology, Madras, India</affiliation>
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<name type="personal"><namePart type="given">Bernhard</namePart>
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<affiliation>University of Dortmund, Dortmund, Germany</affiliation>
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<name type="personal"><namePart type="given">Madhu</namePart>
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<affiliation>Massachusetts Institute of Technology, MA, USA</affiliation>
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<name type="personal"><namePart type="given">Demetri</namePart>
<namePart type="family">Terzopoulos</namePart>
<affiliation>University of California, Los Angeles, CA, USA</affiliation>
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<name type="personal"><namePart type="given">Doug</namePart>
<namePart type="family">Tygar</namePart>
<affiliation>University of California, Berkeley, CA, USA</affiliation>
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<name type="personal"><namePart type="given">Moshe</namePart>
<namePart type="given">Y.</namePart>
<namePart type="family">Vardi</namePart>
<affiliation>Rice University, Houston, TX, USA</affiliation>
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<name type="personal"><namePart type="given">Gerhard</namePart>
<namePart type="family">Weikum</namePart>
<affiliation>Max-Planck Institute of Computer Science, Saarbrücken, Germany</affiliation>
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