diplomsko delo
Žiga Pušnik (Author), Marko Robnik Šikonja (Mentor)

Abstract

Cilj diplomske naloge je preizkusiti učenje jezikovnih problemov s pomočjo globokih konvolucijskih nevronskih mrež. Konvolucijske nevronske mreže so bile razvite predvsem za področje umetnega zaznavanja in delujejo na podlagi konvolucije. Naučili smo jih, da so na podlagi kratkega povzetka besedila napovedale razred, h kateremu spada. Drugi problem, ki smo ga reševali je postavljanje vejic v slovenskem jeziku. Konvolucijsko nevronsko mrežo smo sprogramirali s programskim jezikom python. Uporabili smo knjižnjico Theano. Izhajali smo iz že obstoječih raziskav. Opišemo način, kako smo obdelali podatkovne množice, da so primerne za naš model. Opravili smo več poskusov. Primerjali smo lematizacijo in krnjenje ter predstavitev besedila z vektorizacijo in predstavitev z bitnim poljem. Zadovoljive rezultate smo dobili, če smo besedilo kvantizirali, kjer smo črke vektorizirali z 1 do m kodiranjem. Naši rezultati pri postavljanju vejic so primerljivi z rezultati drugih raziskav.

Keywords

strojno učenje;obdelava naravnega jezika;nevronska mreža;nevron;konvolucija;konvolucijska nevronska mreža;klasifikacija;klasifikacijski model;klasifikator;klasifikacijska točnost;jezik;besedilo;vejica;lema;krn;moment;gradient;gradientni spust;vzvratno širjenje napake;učenje;stopnja učenja;podatkovna množica;jezikovni korpus;atribut;računalništvo;računalništvo in informatika;univerzitetni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [Ž. Pušnik]
UDC: 004.85:81'322.2(043.2)
COBISS: 1536476611 Link will open in a new window
Views: 1858
Downloads: 613
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: English
Secondary title: Using deep convolutional neural networks on natural language problems
Secondary abstract: The thesis examines the learning of language problems with convolutional neural networks. Convolutional neural networks were developed for machine vision. We used them to classify short abstracts and to learn a comma placement in Slovenian language. We programmed our convolutional neural network in programming language python with Theano library. Our work is based on existing research. We describe adaptation of datasets to our model. Several experiments were conducted and we compared lemmatization versus stemming and vector representation of text versus byte array representation. The best results were obtained with text quantized with 1 to m encoding. Comma placing results are comparable with other machine learning approaches.
Secondary keywords: machine learning;natural language processing;neural network;neuron;convolution;convolutional neural network;classification;classification model;classificator;classification accuracy;language;text;comma;lemma;stemm;momentum;gradient;gradient descent;backpropagation;learning rate;momentum rate;dataset;text corpus;attribute;computer science;computer and information science;diploma;
File type: application/pdf
Type (COBISS): Bachelor thesis/paper
Study programme: 1000468
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 47 str.
ID: 8890380