Learning multilingual morphology with Clog
Identifieur interne : 004109 ( Istex/Corpus ); précédent : 004108; suivant : 004110Learning multilingual morphology with Clog
Auteurs : Suresh Manandhar ; Sašo Džeroski ; Tomaž ErjavecSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: The paper presents the decision list learning system Clog and the results of using it to learn nominal inflections of English, Romanian, Czech, Slovene, and Estonian. The dataset used to induce rules for the synthesis and analysis of the inflectional paradigms of nouns and adjectives of these languages is the Multext-East multilingual tagged corpus. The ILP system FoIDL is also applied to the same dataset, and this paper compares the induction methodology and results of the two systems. The experiment shows that the accuracy of the two systems is comparable when using the same training set. However, while FOIDL is, due to efficiency reasons, severely limited in the size of the training set, CLOG does not suffer from such limitations. With the increase of the training set size possible with CLOG, it significantly outperforms FOIDL and learns highly accurate morphological rules.
Url:
DOI: 10.1007/BFb0027317
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The dataset used to induce rules for the synthesis and analysis of the inflectional paradigms of nouns and adjectives of these languages is the Multext-East multilingual tagged corpus. The ILP system FoIDL is also applied to the same dataset, and this paper compares the induction methodology and results of the two systems. The experiment shows that the accuracy of the two systems is comparable when using the same training set. However, while FOIDL is, due to efficiency reasons, severely limited in the size of the training set, CLOG does not suffer from such limitations. With the increase of the training set size possible with CLOG, it significantly outperforms FOIDL and learns highly accurate morphological rules.</div></front></TEI><istex><corpusName>springer-ebooks</corpusName><author><json:item><name>Suresh Manandhar</name><affiliations><json:string>Intelligent Systems Group, Department of Computer Science, University of York, YO10 5DD, York, UK</json:string><json:string>E-mail: Suresh@cs.york.ac.uk</json:string></affiliations></json:item><json:item><name>Sašo Džeroski</name><affiliations><json:string>Department for Intelligent Systems, Jožef Stefan Institute, Jamova 39, SI-1000, Ljubljana, Slovenia</json:string><json:string>E-mail: Saso.Dzeroski@ijs.si</json:string></affiliations></json:item><json:item><name>Tomaž Erjavec</name><affiliations><json:string>Department for Intelligent Systems, Jožef Stefan Institute, Jamova 39, SI-1000, Ljubljana, Slovenia</json:string><json:string>E-mail: Tomaz.Erjavec@ijs.si</json:string></affiliations></json:item></author><arkIstex>ark:/67375/HCB-5RJC4Q4C-8</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>ReviewPaper</json:string></originalGenre><abstract>Abstract: The paper presents the decision list learning system Clog and the results of using it to learn nominal inflections of English, Romanian, Czech, Slovene, and Estonian. 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The dataset used to induce rules for the synthesis and analysis of the inflectional paradigms of nouns and adjectives of these languages is the <hi rend="smallCaps">Multext</hi>-East multilingual tagged corpus. The ILP system FoIDL is also applied to the same dataset, and this paper compares the induction methodology and results of the two systems. The experiment shows that the accuracy of the two systems is comparable when using the same training set. However, while FOIDL is, due to efficiency reasons, severely limited in the size of the training set, CLOG does not suffer from such limitations. With the increase of the training set size possible with CLOG, it significantly outperforms FOIDL and learns highly accurate morphological rules.</p></abstract><textClass ana="subject"><keywords scheme="book-subject-collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject"><list><label>I</label><item><term type="Primary">Computer Science</term></item><label>I21017</label><item><term type="Secondary" subtype="priority-1">Artificial Intelligence (incl. 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The dataset used to induce rules for the synthesis and analysis of the inflectional paradigms of nouns and adjectives of these languages is the <Emphasis Type="SmallCaps">Multext</Emphasis>-East multilingual tagged corpus. The ILP system FoIDL is also applied to the same dataset, and this paper compares the induction methodology and results of the two systems. The experiment shows that the accuracy of the two systems is comparable when using the same training set. However, while FOIDL is, due to efficiency reasons, severely limited in the size of the training set, CLOG does not suffer from such limitations. With the increase of the training set size possible with CLOG, it significantly outperforms FOIDL and learns highly accurate morphological rules.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Learning multilingual morphology with Clog</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Learning multilingual morphology with Clog </title></titleInfo><name type="personal"><namePart type="given">Suresh</namePart><namePart type="family">Manandhar</namePart><affiliation>Intelligent Systems Group, Department of Computer Science, University of York, YO10 5DD, York, UK</affiliation><affiliation>E-mail: Suresh@cs.york.ac.uk</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Sašo</namePart><namePart type="family">Džeroski</namePart><affiliation>Department for Intelligent Systems, Jožef Stefan Institute, Jamova 39, SI-1000, Ljubljana, Slovenia</affiliation><affiliation>E-mail: Saso.Dzeroski@ijs.si</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Tomaž</namePart><namePart type="family">Erjavec</namePart><affiliation>Department for Intelligent Systems, Jožef Stefan Institute, Jamova 39, SI-1000, Ljubljana, Slovenia</affiliation><affiliation>E-mail: Tomaz.Erjavec@ijs.si</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="ReviewPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">1998</dateIssued><copyrightDate encoding="w3cdtf">1998</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: The paper presents the decision list learning system Clog and the results of using it to learn nominal inflections of English, Romanian, Czech, Slovene, and Estonian. The dataset used to induce rules for the synthesis and analysis of the inflectional paradigms of nouns and adjectives of these languages is the Multext-East multilingual tagged corpus. The ILP system FoIDL is also applied to the same dataset, and this paper compares the induction methodology and results of the two systems. The experiment shows that the accuracy of the two systems is comparable when using the same training set. However, while FOIDL is, due to efficiency reasons, severely limited in the size of the training set, CLOG does not suffer from such limitations. With the increase of the training set size possible with CLOG, it significantly outperforms FOIDL and learns highly accurate morphological rules.</abstract><relatedItem type="host"><titleInfo><title>Inductive Logic Programming</title><subTitle>8th International Conference, ILP-98 Madison, Wisconsin, USA, July 22–24, 1998 Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Page</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">1998</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I14010">Programming Techniques</topic></subject><identifier type="DOI">10.1007/BFb0027303</identifier><identifier type="ISBN">978-3-540-64738-6</identifier><identifier type="eISBN">978-3-540-69059-7</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">55362</identifier><identifier type="BookID">3540647384</identifier><identifier type="BookChapterCount">30</identifier><identifier type="BookVolumeNumber">1446</identifier><part><date>1998</date><detail type="volume"><number>1446</number><caption>vol.</caption></detail><extent unit="pages"><start>135</start><end>144</end></extent></part><recordInfo><recordOrigin>Springer-Verlag, 1998</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title><subTitle>Lecture Notes in Artificial Intelligence</subTitle></titleInfo><name type="personal"><namePart type="given">G.</namePart><namePart type="family">Goos</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">J.</namePart><namePart type="family">Hartmanis</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">J.</namePart><namePart type="family">van Leeuwen</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jaime</namePart><namePart type="given">G.</namePart><namePart type="family">Carbonell</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jörg</namePart><namePart type="family">Siekmann</namePart><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">1998</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag, 1998</recordOrigin></recordInfo></relatedItem><identifier type="istex">F948188A2E4288A49CC3B8F2A962AC2C1C57C4AB</identifier><identifier type="ark">ark:/67375/HCB-5RJC4Q4C-8</identifier><identifier type="DOI">10.1007/BFb0027317</identifier><identifier type="ChapterID">14</identifier><identifier type="ChapterID">Chap14</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag, 1998</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-RLRX46XW-4">springer</recordContentSource><recordOrigin>Springer-Verlag, 1998</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/ark:/67375/HCB-5RJC4Q4C-8/record.json</uri></json:item></metadata></istex></record>Pour manipuler ce document sous Unix (Dilib)
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