Višeoznačna klasifikacija dokumenata u pojmovniku EuroVoc

master's thesis

• Title (english): Multi-label Document Classification using EuroVoc Thesaurus
• Author: Martin Tutek
• Mentor: Jan Šnajder (mentor)
• Committee member: Jan Šnajder (predsjednik povjerenstva)
• Committee member: Marin Golub (član povjerenstva)
• Committee member: Nikola Bogunović (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2014-07-08, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

EuroVoc je višejezični konceptualni pojmovnik pravnih dokumenata zemalja članica Europske Unije.
Sastoji se od preko 6000 različitih oznaka klasa koje se dodjeljuju dokumentima, zadatak koji
spada u područje strojnog učenja. Problem pojmovnika EuroVoc je visoko rasipanje oznaka te njihova
neravnomjerna raspodjeljenost. Slabije reprezentirane oznake su teže za prepoznati i klasificirati,
te najviše škode procesu klasifikacije. Razni načini poboljšanja postupka su analizirani s naglaskom
na povezivanje sličnih oznaka u grupe te potom strožu diskriminaciju među članovima iste grupe.
Rezultati su uspoređeni s osnovnim algoritmom stroja s potpornim vektorima, te su izneseni zaključci
i analizirani mogući novi smjerovi istraživanja.

Abstract (english)

EuroVoc is a multilingual conceptual thesaurus covering the activities of the European Union.
It consists of over 6000 different class labels which are assigned to documents, a problem which
is tackled by machine learning algorithms for multi-label classification. Problems with the
thesaurus include label sparsity, inconsistent descriptor distribution and various other.
The main line of research was an attempt to group similar labels in order to split the problem
into differentiating between groups of labels and differentiating between similar labels. The
model was compared to the SVM baseline and various other multi-label classification algorithms,
the results and possible new lines of research were analysed.

• Keywords: EuroVoc; klasifikacija teksta; neravnomjerna distribucija klasa; strojno učenje
• Keywords (english): EuroVoc; text classification; Multi-label classification; Label sparsity; Machine Learning
• Language: english
• URN:NBN: urn:nbn:hr:168:151199
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2018-02-06 14:12:39