| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Show document

Title:S strojnim učenjem podprta analiza znanstvenih revij in avtorjev : magistrsko delo
Authors:Šart, Tevž (Author)
Karakatič, Sašo (Mentor) More about this mentor... New window
Files:.pdf MAG_Sart_Tevz_2021.pdf (1,52 MB)
MD5: 7B7C97079ACCD95462C91E7AC84EF435
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V sklopu magistrske naloge smo se osredotočili na problematiko iskanja primernih revij za objavo znanstvenih člankov različnih avtorjev. V prvem delu smo se osredotočili na pridobivanje znanja iz nestrukturiranih podatkov. Za pridobivanje uporabnega znanja smo uporabili način besedne vložitve. V drugem delu smo se osredotočili na izgradnjo programske rešitve za vektorizacijo znanstvenih člankov in revij. Namen magistrske je bil ugotoviti, ali lahko s pomočjo strojnega učenja in tehnike vektorizacije besedila ugotovimo podobnosti med znanstvenimi članki različnih avtorjev in revij ter na takšen način ugotovimo, ali avtor objavlja svoje znanstvene članke v pravilnih revijah. Vhodni korpus smo pridobili iz spletne baze znanstvenih člankov Scopus. S pomočjo rezultatov programske rešitve smo opravili analizo, s pomočjo katere smo pridobili odgovore na zastavljena raziskovalna vprašanja ter posledično sprejeli ali zavrgli hipoteze.
Keywords:doc2vec, tf-idf, besedne vložitve, vektorizacija besedila, obdelava naravnega jezika
Year of publishing:2021
Place of performance:Maribor
Publisher:[T. Šart]
Number of pages:IX, 59 f.
Source:Maribor
UDC:004.85:004.775(043.2)
COBISS_ID:60445699 New window
NUK URN:URN:SI:UM:DK:GVEYXNJC
Views:98
Downloads:14
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
:
  
Average score:(0 votes)
Your score:Voting is allowed only for logged in users.
Share:AddThis
AddThis uses cookies that require your consent. Edit consent...

Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:23.03.2021

Secondary language

Language:English
Title:Machine learning based analysis of scientific journals and authors
Abstract:As part of the master's thesis, we focused on the issue of finding suitable journals for the publication of scientific articles by various authors. In the first part, we focused on acquiring knowledge from unstructured data. We used the word embedding method to gain useful knowledge. In the second part, we focused on building a software solution for vectorization of scientific articles and journals. The purpose of the master's thesis was to determine whether we can use machine learning and text vectorization techniques to determine the similarities between scientific articles of different authors and journals and thus determine whether the author publishes his scientific articles in the correct journals. The input corpus was obtained from the online database of scientific articles Scoupus. With the help of the results of the software solution, we performed an analysis with the help of which we obtained answers to the posed research questions and consequently accepted or rejected the set hypotheses.
Keywords:doc2vec, tf-idf, Word embedding, text vectorization, natural language processing


Comments

Leave comment

You have to log in to leave a comment.

Comments (0)
0 - 0 / 0
 
There are no comments!

Back
Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica