Automatic Discovery and Aggregation of Compound Names for the Use in Knowledge Representations

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dc.creatorBiemann,Christian
dc.creatorQuasthoff,Uwe
dc.creatorBöhm,Karsten
dc.creatorWolff,Christian
dc.date2003
dc.date.accessioned2024-02-06T12:52:21Z
dc.date.available2024-02-06T12:52:21Z
dc.descriptionAutomatic acquisition of information structures like Topic Maps or semantic networks from large document collections is an important issue in knowledge management. An inherent problem with automatic approaches is the treatment of multiword terms as single semantic entities. Taking company names as an example, we present a method for learning multiword terms from large text corpora exploiting their internal structure. Through the iteration of a search step and a verification step the single words typically forming company names are learnt. These name elements are used for recognizing compounds in order to use them for further processing. We give some evaluation of experiments on company name extraction and discuss some applications.
dc.formattext/html
dc.identifierhttps://doi.org/10.3217/jucs-009-06-0530
dc.identifierhttps://lib.jucs.org/article/28035/
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/8348
dc.languageen
dc.publisherJournal of Universal Computer Science
dc.relationinfo:eu-repo/semantics/altIdentifier/eissn/0948-6968
dc.relationinfo:eu-repo/semantics/altIdentifier/pissn/0948-695X
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsJ.UCS License
dc.sourceJUCS - Journal of Universal Computer Science 9(6): 530-541
dc.subjectcorpora
dc.subjectsemantic relations
dc.subjecttopic maps
dc.subjecttext mining
dc.subjectknowledge management
dc.subjectnamed entity extraction
dc.titleAutomatic Discovery and Aggregation of Compound Names for the Use in Knowledge Representations
dc.typeResearch Article
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