Learning for Text Categorization and Information Extraction with ILP
Identifieur interne : 001C37 ( Main/Exploration ); précédent : 001C36; suivant : 001C38Learning for Text Categorization and Information Extraction with ILP
Auteurs : Markus Junker [Allemagne] ; Michael Sintek [Allemagne] ; Matthias Rinck [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2000.
Abstract
Abstract: Text Categorization (TC) and Information Extraction (IE) are two important goals of Natural Language Processing. While handcrafting rules for both tasks has a long tradition, learning approaches used to gain much interest in the past. Since in both tasks text as a sequence of words is of crucial importance, propositional learners have strong limitations. Although viewing learning for TC and IE as ILPproblems is obvious, most approaches rather use proprietary formalisms. In the present paper we try to provide a solid basis for the application of ILPmetho ds to these learning problems. We introduce three basic types (namely a type for text, one for words and one for positions in texts) and three simple predicate definitions over these types which enable us to write text categorization and information extraction rules as logic programs. Based on the proposed representation, we present an approach to the problem of learning rules for TC and IE in terms of ILP.We conclude the paper by comparing our approach of representing texts and rules as logic programs to others.
Url:
DOI: 10.1007/3-540-40030-3_16
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 001D69
- to stream Istex, to step Curation: 001C47
- to stream Istex, to step Checkpoint: 001239
- to stream Main, to step Merge: 001D36
- to stream Main, to step Curation: 001C37
Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct:series"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Learning for Text Categorization and Information Extraction with ILP</title><author><name sortKey="Junker, Markus" sort="Junker, Markus" uniqKey="Junker M" first="Markus" last="Junker">Markus Junker</name></author><author><name sortKey="Sintek, Michael" sort="Sintek, Michael" uniqKey="Sintek M" first="Michael" last="Sintek">Michael Sintek</name></author><author><name sortKey="Rinck, Matthias" sort="Rinck, Matthias" uniqKey="Rinck M" first="Matthias" last="Rinck">Matthias Rinck</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:5822586AE42C1D7515FA2548B650B21CC8105217</idno><date when="2000" year="2000">2000</date><idno type="doi">10.1007/3-540-40030-3_16</idno><idno type="url">https://api.istex.fr/document/5822586AE42C1D7515FA2548B650B21CC8105217/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001D69</idno><idno type="wicri:Area/Istex/Curation">001C47</idno><idno type="wicri:Area/Istex/Checkpoint">001239</idno><idno type="wicri:doubleKey">0302-9743:2000:Junker M:learning:for:text</idno><idno type="wicri:Area/Main/Merge">001D36</idno><idno type="wicri:Area/Main/Curation">001C37</idno><idno type="wicri:Area/Main/Exploration">001C37</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Learning for Text Categorization and Information Extraction with ILP</title><author><name sortKey="Junker, Markus" sort="Junker, Markus" uniqKey="Junker M" first="Markus" last="Junker">Markus Junker</name><affiliation wicri:level="1"><country xml:lang="fr">Allemagne</country><wicri:regionArea>German Research Center for Artificial Intelligence (DFKI GmbH), P.O. Box 2080, 67608, Kaiserslautern</wicri:regionArea><wicri:noRegion>67608, Kaiserslautern</wicri:noRegion><wicri:noRegion>Kaiserslautern</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Allemagne</country></affiliation></author><author><name sortKey="Sintek, Michael" sort="Sintek, Michael" uniqKey="Sintek M" first="Michael" last="Sintek">Michael Sintek</name><affiliation wicri:level="1"><country xml:lang="fr">Allemagne</country><wicri:regionArea>German Research Center for Artificial Intelligence (DFKI GmbH), P.O. Box 2080, 67608, Kaiserslautern</wicri:regionArea><wicri:noRegion>67608, Kaiserslautern</wicri:noRegion><wicri:noRegion>Kaiserslautern</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Allemagne</country></affiliation></author><author><name sortKey="Rinck, Matthias" sort="Rinck, Matthias" uniqKey="Rinck M" first="Matthias" last="Rinck">Matthias Rinck</name><affiliation wicri:level="1"><country xml:lang="fr">Allemagne</country><wicri:regionArea>German Research Center for Artificial Intelligence (DFKI GmbH), P.O. Box 2080, 67608, Kaiserslautern</wicri:regionArea><wicri:noRegion>67608, Kaiserslautern</wicri:noRegion><wicri:noRegion>Kaiserslautern</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Allemagne</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2000</date></imprint><idno type="ISSN">0302-9743</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">5822586AE42C1D7515FA2548B650B21CC8105217</idno><idno type="DOI">10.1007/3-540-40030-3_16</idno><idno type="ChapterID">16</idno><idno type="ChapterID">Chap16</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Text Categorization (TC) and Information Extraction (IE) are two important goals of Natural Language Processing. While handcrafting rules for both tasks has a long tradition, learning approaches used to gain much interest in the past. Since in both tasks text as a sequence of words is of crucial importance, propositional learners have strong limitations. Although viewing learning for TC and IE as ILPproblems is obvious, most approaches rather use proprietary formalisms. In the present paper we try to provide a solid basis for the application of ILPmetho ds to these learning problems. We introduce three basic types (namely a type for text, one for words and one for positions in texts) and three simple predicate definitions over these types which enable us to write text categorization and information extraction rules as logic programs. Based on the proposed representation, we present an approach to the problem of learning rules for TC and IE in terms of ILP.We conclude the paper by comparing our approach of representing texts and rules as logic programs to others.</div></front></TEI><affiliations><list><country><li>Allemagne</li></country></list><tree><country name="Allemagne"><noRegion><name sortKey="Junker, Markus" sort="Junker, Markus" uniqKey="Junker M" first="Markus" last="Junker">Markus Junker</name></noRegion><name sortKey="Junker, Markus" sort="Junker, Markus" uniqKey="Junker M" first="Markus" last="Junker">Markus Junker</name><name sortKey="Rinck, Matthias" sort="Rinck, Matthias" uniqKey="Rinck M" first="Matthias" last="Rinck">Matthias Rinck</name><name sortKey="Rinck, Matthias" sort="Rinck, Matthias" uniqKey="Rinck M" first="Matthias" last="Rinck">Matthias Rinck</name><name sortKey="Sintek, Michael" sort="Sintek, Michael" uniqKey="Sintek M" first="Michael" last="Sintek">Michael Sintek</name><name sortKey="Sintek, Michael" sort="Sintek, Michael" uniqKey="Sintek M" first="Michael" last="Sintek">Michael Sintek</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/OcrV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001C37 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 001C37 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Ticri/CIDE
|area= OcrV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= ISTEX:5822586AE42C1D7515FA2548B650B21CC8105217
|texte= Learning for Text Categorization and Information Extraction with ILP
}}
| This area was generated with Dilib version V0.6.32. |  |