Automatic Taxonomy Induction based on Word-embedding of Neural Nets
Zafar, B., Imran, A., Asghar, M., Cochez, M., & Hämäläinen, T. (2018). Automatic Taxonomy Induction based on Word-embedding of Neural Nets. International Journal of Digital Content Technology and its Applications, 12 (1), 45-54. Retrieved from http://www.globalcis.org/jdcta/ppl/JDCTA3820PPL.pdf
Julkaistu sarjassa
International Journal of Digital Content Technology and its ApplicationsPäivämäärä
2018Oppiaine
TietotekniikkaTekijänoikeudet
© the Authors & GlobalCIS, 2018.
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.
...
Julkaisija
Convergence Information Society (GlobalCIS)ISSN Hae Julkaisufoorumista
1975-9339Asiasanat
Alkuperäislähde
http://www.globalcis.org/jdcta/ppl/JDCTA3820PPL.pdfMetadata
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