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Author:Soppela, Jyri
Title:Nonnegative Matrix Factorization in Text Mining Applications
Ohjaamattoman koneoppimisen menetelmät luonnollisen kielen tilastollisen analyysin apuna
Publication type:Master's thesis
Publication year:2014
Pages:vi + 45      Language:   eng
Department/School:Sähkötekniikan korkeakoulu
Main subject:Computer and information science   (T-61)
Supervisor:Oja, Erkki
Instructor:Vigário, Ricardo
Electronic version URL: http://urn.fi/URN:NBN:fi:aalto-201502191893
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Location:P1 Ark Aalto  2649   | Archive
Keywords:nonnegative matrix factorization
text mining
NMF
tiedonlouhinta
tf-idf
Abstract (eng):Meta-analysis of scientific publications is a practice where conclusions, sometimes novel, are drawn from already published material.
It is mostly done by hand but on some fields, automatic tools have appeared to mine through large amounts of scientific literature.
In this thesis, methods in statistical processing of natural language are used to process neuroscience articles.
The long-time goal in which this thesis is a part is to construct a method to automatically process neuroscience publications and possibly by combining data in them, find new results not found by the original authors.
Two computational methods, k-means clustering and non-negative matrix factorization, were used on several text data data sets to find semantic structure in them.
The results using the computational methods were not very useful but proved that the tf-idf feature extraction method has potential.
The clustering performed better than random assignment of clusters and published literature has presented even higher results using the same methods with different parameters.
Abstract (fin):Tieteellisten julkaisujen meta-analyysi on käytäntö, jossa jo julkaistusta materiaalista tehdään johtopäätöksiä.
Joissain tapauksissa voidaan tehdä jopa alkuperäisessa aineistossa julkaisemattomia löydöksiä.
Meta-analyysiä tehdään paljon ihmisvoimin, mutta joillain aloilla on otettu käyttöön automaattisia työkaluja suurten aineistojen läpikäyntiin.
Tässä työssä luonnollisen kielen tilastollisia menetelmiä käytetään neurotiedeartikkelien prosessointiin.
Pitkän aikavälin tavoite, jonka osa tämä työ on, on löytää jo julkaistusta neurotiedekirjallisuudesta tietoa, jota ei voitaisi päätellä yksittäisistä artikkeleista.
Kahta ohjaamatonta laskennallista metodia, k-means-klusterointia ja NMF-matriisihajotelmaa, käytettiin usean eri aineiston käsittelyyn semanttisen rakenteen löytämiseksi.
Laskennallisten metodien tulokset eivät olleet odotetun tasoisia, mutta tf-idf-piirre-erottelun käyttökelpoisuus validoitiin.
Klusteroinnit toimivat satunnaista klusterointia paremmin ja julkaistussa kirjallisuudessa on onnistuttu tuottamaan samoilla metodeilla parempia tuloksia eri parametreja käyttäen.
ED:2015-03-08
INSSI record number: 50633
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