Machine learning for text indexing [Elektronische Ressource] : concept extraction, keyword extraction and tag recommendation / vorgelegt von Hendri Murfi

Machine Learning for Text IndexingConcept Extraction, Keyword Extraction and Tag Recommendationvorgelegt vonMaster of ScienceHendri Mur aus Jakarta, IndonesienVon der Fakultat IV { Elektrotechnik und Informatikder Technischen Universitat Berlinzur Erlangung des akademischen GradesDoktor der NaturwissenschaftenDr. rer. nat.genehmigte DissertationPromotionsausschuss:Vorsitzender: Prof. Dr. rer. nat. Volker MarklBerichter: Prof. Dr. rer. nat. Klaus ObermayerProf. Dr. -Ing. Sahin AlbayrakTag der wissenschaftlichen Aussprache: 31. August 2010Berlin 2010D 83AbstractDue to some drawbacks, mainly because of semantic issues such as synonymyand polysemy, people consider some approaches to improve the performanceof full-text indexing. The alternative approaches include latent semantic in-dexing, keyword indexing, social indexing (web 2.0) and linked data-basedindexing (semantic web). The aim of this dissertation is to investigate theapplicationsofmachinelearningmethodsforthealternativeapproaches. Theapp areas are concept extraction, keyword extraction and tag recom-mendation.Firstly,weproposeanewlearningmethodcalledtwo-level learning hierar-chy (TLLH) to extract concepts from tagged textual contents. This learningmethod executes separately the existing textual sources, i.e. the user-createdtags and the textual contents.