Learning to Lemmatise Slovene Words
Identifieur interne : 000431 ( Istex/Corpus ); précédent : 000430; suivant : 000432Learning to Lemmatise Slovene Words
Auteurs : Sašo Džeroski ; Tomaž ErjavecSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2000.
Abstract
Abstract: Automatic lemmatisation is a core application for many language processing tasks. In inflectionally rich languages, such as Slovene, assigning the correct lemma to each word in a running text is not trivial: nouns and adjectives, for instance, inflect for number and case, with a complex configuration of endings and stem modifications. The problem is especially difficult for unknown words, as word forms cannot be matched against a lexicon giving the correct lemma, its part-of-speech and paradigm class. The paper discusses a machine learning approach to the automatic lemmatisation of unknown words, in particular nouns and adjectives, in Slovene texts. We decompose the problem of learning to perform lemmatisation into two subproblems: the first is to learn to perform morphosyntactic tagging, and the second is to learn to perform morphological analysis, which produces the lemma from the word form given the correct morphosyntactic tag. A statistics-based trigram tagger is used to learn to perform morphosyntactic tagging and a first-order decision list learning system is used to learn rules for morphological analysis. The dataset used is the 90.000 word Slovene translation of Orwell’s ‘1984’, split into a training and validation set. The validation set is the Appendix of the novel, on which extensive testing of the two components, singly and in combination, is performed. The trained model is then used on an open-domain testing set, which has 25.000 words, pre-annotated with their word lemmas. Here 13.000 nouns or adjective tokens are previously unseen cases. Tested on these unknown words, our method achieves an accuracy of 81% on the lemmatisation task.
Url:
DOI: 10.1007/3-540-40030-3_5
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In inflectionally rich languages, such as Slovene, assigning the correct lemma to each word in a running text is not trivial: nouns and adjectives, for instance, inflect for number and case, with a complex configuration of endings and stem modifications. The problem is especially difficult for unknown words, as word forms cannot be matched against a lexicon giving the correct lemma, its part-of-speech and paradigm class. The paper discusses a machine learning approach to the automatic lemmatisation of unknown words, in particular nouns and adjectives, in Slovene texts. We decompose the problem of learning to perform lemmatisation into two subproblems: the first is to learn to perform morphosyntactic tagging, and the second is to learn to perform morphological analysis, which produces the lemma from the word form given the correct morphosyntactic tag. A statistics-based trigram tagger is used to learn to perform morphosyntactic tagging and a first-order decision list learning system is used to learn rules for morphological analysis. The dataset used is the 90.000 word Slovene translation of Orwell’s ‘1984’, split into a training and validation set. The validation set is the Appendix of the novel, on which extensive testing of the two components, singly and in combination, is performed. The trained model is then used on an open-domain testing set, which has 25.000 words, pre-annotated with their word lemmas. Here 13.000 nouns or adjective tokens are previously unseen cases. Tested on these unknown words, our method achieves an accuracy of 81% on the lemmatisation task.</div></front></TEI><istex><corpusName>springer</corpusName><author><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><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: Automatic lemmatisation is a core application for many language processing tasks. 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A statistics-based trigram tagger is used to learn to perform morphosyntactic tagging and a first-order decision list learning system is used to learn rules for morphological analysis. The dataset used is the 90.000 word Slovene translation of Orwell’s ‘1984’, split into a training and validation set. The validation set is the Appendix of the novel, on which extensive testing of the two components, singly and in combination, is performed. The trained model is then used on an open-domain testing set, which has 25.000 words, pre-annotated with their word lemmas. Here 13.000 nouns or adjective tokens are previously unseen cases. 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In inflectionally rich languages, such as Slovene, assigning the correct lemma to each word in a running text is not trivial: nouns and adjectives, for instance, inflect for number and case, with a complex configuration of endings and stem modifications. The problem is especially difficult for unknown words, as word forms cannot be matched against a lexicon giving the correct lemma, its part-of-speech and paradigm class. The paper discusses a machine learning approach to the automatic lemmatisation of unknown words, in particular nouns and adjectives, in Slovene texts. We decompose the problem of learning to perform lemmatisation into two subproblems: the first is to learn to perform morphosyntactic tagging, and the second is to learn to perform morphological analysis, which produces the lemma from the word form given the correct morphosyntactic tag. A statistics-based trigram tagger is used to learn to perform morphosyntactic tagging and a first-order decision list learning system is used to learn rules for morphological analysis. The dataset used is the 90.000 word Slovene translation of Orwell’s ‘1984’, split into a training and validation set. The validation set is the Appendix of the novel, on which extensive testing of the two components, singly and in combination, is performed. The trained model is then used on an open-domain testing set, which has 25.000 words, pre-annotated with their word lemmas. Here 13.000 nouns or adjective tokens are previously unseen cases. Tested on these unknown words, our method achieves an accuracy of 81% on the lemmatisation task.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I21017</label><label>I16048</label><item><term>Computer Science</term></item><item><term>Artificial Intelligence (incl. 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In inflectionally rich languages, such as Slovene, assigning the correct lemma to each word in a running text is not trivial: nouns and adjectives, for instance, inflect for number and case, with a complex configuration of endings and stem modifications. The problem is especially difficult for unknown words, as word forms cannot be matched against a lexicon giving the correct lemma, its part-of-speech and paradigm class. The paper discusses a machine learning approach to the automatic lemmatisation of unknown words, in particular nouns and adjectives, in Slovene texts. We decompose the problem of learning to perform lemmatisation into two subproblems: the first is to learn to perform morphosyntactic tagging, and the second is to learn to perform morphological analysis, which produces the lemma from the word form given the correct morphosyntactic tag. A statistics-based trigram tagger is used to learn to perform morphosyntactic tagging and a first-order decision list learning system is used to learn rules for morphological analysis. The dataset used is the 90.000 word Slovene translation of Orwell’s ‘1984’, split into a training and validation set. The validation set is the Appendix of the novel, on which extensive testing of the two components, singly and in combination, is performed. The trained model is then used on an open-domain testing set, which has 25.000 words, pre-annotated with their word lemmas. Here 13.000 nouns or adjective tokens are previously unseen cases. Tested on these unknown words, our method achieves an accuracy of 81% on the lemmatisation task.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book><SubSeries><SubSeriesInfo><SubSeriesID>1244</SubSeriesID><SubSeriesTitle Language="En">Lecture Notes in Artificial Intelligence</SubSeriesTitle><SubSeriesSubTitle Language="En">Subseries of Lecture Notes in Computer Science</SubSeriesSubTitle></SubSeriesInfo><SubSeriesHeader><EditorGroup><Editor AffiliationIDS="Aff3"><EditorName DisplayOrder="Western"><GivenName>Jaime</GivenName><GivenName>G.</GivenName><FamilyName>Carbonell</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff4"><EditorName DisplayOrder="Western"><GivenName>Jörg</GivenName><FamilyName>Siekmann</FamilyName></EditorName></Editor><Affiliation ID="Aff3"><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SubSeriesHeader></SubSeries></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Learning to Lemmatise Slovene Words</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Learning to Lemmatise Slovene Words</title></titleInfo><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 type="research-article" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2002-02-01</dateIssued><copyrightDate encoding="w3cdtf">2000</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Automatic lemmatisation is a core application for many language processing tasks. In inflectionally rich languages, such as Slovene, assigning the correct lemma to each word in a running text is not trivial: nouns and adjectives, for instance, inflect for number and case, with a complex configuration of endings and stem modifications. The problem is especially difficult for unknown words, as word forms cannot be matched against a lexicon giving the correct lemma, its part-of-speech and paradigm class. The paper discusses a machine learning approach to the automatic lemmatisation of unknown words, in particular nouns and adjectives, in Slovene texts. We decompose the problem of learning to perform lemmatisation into two subproblems: the first is to learn to perform morphosyntactic tagging, and the second is to learn to perform morphological analysis, which produces the lemma from the word form given the correct morphosyntactic tag. A statistics-based trigram tagger is used to learn to perform morphosyntactic tagging and a first-order decision list learning system is used to learn rules for morphological analysis. The dataset used is the 90.000 word Slovene translation of Orwell’s ‘1984’, split into a training and validation set. The validation set is the Appendix of the novel, on which extensive testing of the two components, singly and in combination, is performed. The trained model is then used on an open-domain testing set, which has 25.000 words, pre-annotated with their word lemmas. Here 13.000 nouns or adjective tokens are previously unseen cases. Tested on these unknown words, our method achieves an accuracy of 81% on the lemmatisation task.</abstract><relatedItem type="host"><titleInfo><title>Learning Language in Logic</title></titleInfo><name type="personal"><namePart type="given">James</namePart><namePart type="family">Cussens</namePart><affiliation>Department of Computer Science, University of York, YO10 5DD, Heslington, York, UK</affiliation><affiliation>E-mail: jc@cs.york.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Sašo</namePart><namePart type="family">Džeroski</namePart><affiliation>Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia</affiliation><affiliation>E-mail: saso.dzeroski@ijs.si</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" displayLabel="Reference work"></genre><originInfo><copyrightDate encoding="w3cdtf">2000</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="I16048">Mathematical Logic and Formal Languages</topic></subject><identifier type="DOI">10.1007/3-540-40030-3</identifier><identifier type="ISBN">978-3-540-41145-1</identifier><identifier type="eISBN">978-3-540-40030-1</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="BookTitleID">62983</identifier><identifier type="BookID">3-540-40030-3</identifier><identifier type="BookChapterCount">18</identifier><identifier type="BookVolumeNumber">1925</identifier><identifier type="BookSequenceNumber">1925</identifier><identifier type="PartChapterCount">3</identifier><part><date>2000</date><detail type="part"><title>2: Morphology & Phonology</title></detail><detail type="volume"><number>1925</number><caption>vol.</caption></detail><extent unit="pages"><start>69</start><end>88</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2000</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></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><originInfo><copyrightDate encoding="w3cdtf">2000</copyrightDate><issuance>serial</issuance></originInfo><relatedItem type="constituent"><titleInfo><title>Lecture Notes in Artificial Intelligence</title><subTitle>Subseries of Lecture Notes in Computer Science</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">James</namePart><namePart type="family">Cussens</namePart><affiliation>Department of Computer Science, University of York, YO10 5DD, Heslington, York, UK</affiliation><affiliation>E-mail: jc@cs.york.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Sašo</namePart><namePart type="family">Džeroski</namePart><affiliation>Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia</affiliation><affiliation>E-mail: saso.dzeroski@ijs.si</affiliation><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><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jörg</namePart><namePart type="family">Siekmann</namePart><affiliation>University of Saarland, Saarbrücken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="sub-series"></genre><identifier type="SubSeriesID">1244</identifier></relatedItem><identifier type="ISSN">0302-9743</identifier><identifier type="SeriesID">558</identifier><part><detail type="volume"><number>2</number><caption>vol.</caption></detail></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2000</recordOrigin></recordInfo></relatedItem><identifier 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