dc.contributor.author | Zafar, Bushra | |
dc.contributor.author | Imran, Ayesha | |
dc.contributor.author | Asghar, Muhammad | |
dc.contributor.author | Cochez, Michael | |
dc.contributor.author | Hämäläinen, Timo | |
dc.date.accessioned | 2018-11-23T05:41:53Z | |
dc.date.available | 2018-11-23T05:41:53Z | |
dc.date.issued | 2018 | fi |
dc.identifier.citation | Zafar, B., Imran, A., Asghar, M., Cochez, M., & Hämäläinen, T. (2018). Automatic Taxonomy Induction based on Word-embedding of Neural Nets. <em>International Journal of Digital Content Technology and its Applications</em>, 12 (1), 45-54. Retrieved from <a href="http://www.globalcis.org/jdcta/ppl/JDCTA3820PPL.pdf">http://www.globalcis.org/jdcta/ppl/JDCTA3820PPL.pdf</a> | fi |
dc.identifier.other | TUTKAID_76297 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/60289 | |
dc.description.abstract | Taxonomy is a knowledge management tool that presents useful information in a well-ordered
structure prevents overloading of information on its access and making the information access
qualitative. This article is concerned with automatically extracting asymmetrical hierarchical
relations from a large corpus and subsequent taxonomy construction by domain independent and
semi-supervised system. The methodology relies on the term’s distributional semantics. The
algorithm utilizes the word-embedding generated from the vector space model. The model is
trained over a large corpus to generate word-embedding of each word in a corpus. Then, the
system finds and extracts the hypernyms by using the genetic algorithm based on distributional
semantics calculations. In the last step, the system adds hyponym-hypernym relations extracted
from the string comparison module. Gold Standards taxonomies are used to evaluate the
system’s taxonomies for each domain. Our system achieved significant results across each
domain. | fi |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Convergence Information Society (GlobalCIS) | |
dc.relation.ispartofseries | International Journal of Digital Content Technology and its Applications | |
dc.relation.uri | http://www.globalcis.org/jdcta/ppl/JDCTA3820PPL.pdf | |
dc.rights | In Copyright | |
dc.subject.other | tiedonlouhinta | fi |
dc.subject.other | tekstinlouhinta | fi |
dc.subject.other | sanasemantiikka | fi |
dc.subject.other | neuroverkot | fi |
dc.subject.other | hyponym-hypernym relations | fi |
dc.subject.other | taxonomy induction | fi |
dc.subject.other | word-embedding | fi |
dc.title | Automatic Taxonomy Induction based on Word-embedding of Neural Nets | fi |
dc.type | article | |
dc.identifier.urn | URN:NBN:fi:jyu-201811224828 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Tietotekniikka | |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.date.updated | 2018-11-22T10:15:12Z | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 45-54 | |
dc.relation.issn | 1975-9339 | |
dc.relation.numberinseries | 1 | |
dc.relation.volume | 12 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © the Authors & GlobalCIS, 2018. | |
dc.rights.accesslevel | openAccess | fi |
dc.format.content | fulltext | |
dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |