| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Show document Help

Title:Vpliv priprave nestrukturiranih podatkov na klasifikacijo : magistrsko delo
Authors:ID Pečnik, Špela (Author)
ID Podgorelec, Vili (Mentor) More about this mentor... New window
Files:.pdf MAG_Pecnik_Spela_2019.pdf (1,49 MB)
MD5: EBA9DB0F5212AE2FD45F57091E57B407
PID: 20.500.12556/dkum/239e9d35-e8c8-4fbf-8461-28041dd87704
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V vsakdanjem življenju se v večini primerov srečujemo z nestrukturiranimi podatki v obliki besedil iz različnih virov. Število teh iz dneva v dan narašča, zato obstaja vse večja potreba po njihovi organizaciji in kategorizaciji. Pri teh podatkih je najpomembnejša njihova predpriprava na uporabo v algoritmih strojnega učenja. Za ustrezno pripravo besedila lahko uporabimo različne metode/tehnike predprocesiranja – besedilo pretvorimo v male črke, iz njega odstranimo stop-besede, nad posameznimi besedami uporabimo krnjenje, lematizacijo, besede sestavljamo v fraze različnih dolžin (uni-grame, bi-grame, tri-grame) ali pa jih na primer pretvorimo v vektorsko obliko (ang. word embedding). S pomočjo laboratorijskega eksperimenta smo ugotovili, da nekatere tehnike predobdelave bolj vplivajo na uspešnost klasifikacije kot druge, poleg tega pa ima velik vpliv na uspešnost klasifikacije sam jezik in količina besedila, ter klasifikator, ki ga uporabimo za strojno učenje.
Keywords:nestrukturirani podatki, klasifikacija besedil, vektorska predstavitev besedil, krnjenje, lematizacija
Place of publishing:Maribor
Place of performance:Maribor
Publisher:Š. Pečnik
Year of publishing:2019
Number of pages:VIII, 74 str.
PID:20.500.12556/DKUM-73712 New window
UDC:004.94:004.83(043.2)
URN:URN:SI:UM:DK:MLJBF77J
COBISS.SI-ID:22489366 New window
NUK URN:URN:SI:UM:DK:MLJBF77J
Publication date in DKUM:14.06.2019
Views:1818
Downloads:271
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
Copy citation
  
Average score:(0 votes)
Your score:Voting is allowed only for logged in users.
Share:Bookmark and Share



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:05.06.2019

Secondary language

Language:English
Title:The impact of preprocessing on the classification of unstructured data
Abstract:In everyday life, in most cases we encounter unstructured data in the form of texts from different sources. The number of these is growing every day, so there is an increasing need for their organization and categorization. For these data, the most important part is their pre-preparation for use in machine learning algorithms. Various methods/techniques of pre-processing can be used for the proper preparation of the text - we can convert the text into lower case letters, remove the stop-words from it, use stemming, lemmatization, compose words in phrases of different lengths (unigrams, bigrams, trigrams), or convert them into word embedding. With the help of a laboratory experiment, we found out that some pre-preparation techniques have a greater impact on the performance of the classification than others, and in addition, the language and quantity of the text, as well as the classifier used for machine learning, have a great influence on the success of the classification.
Keywords:unstructured data, text classification, word embedding, stemming, lemmatization


Comments

Leave comment

You must 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