Using deep convolutional neural networks on natural language problems

Žiga Pušnik (2015) Using deep convolutional neural networks on natural language problems. EngD thesis.

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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.

Item Type:

Thesis (EngD thesis)

Keywords:

machine learning, natural language processing, neural network, neuron, convolution, convolutional neural netvork, clasification, clasification model, clasificator, clasification accuracy, language, text, comma, lemma, stemm, momentum, gradient descent, backpropagation, text corpus , attribute

Number of Pages:

Language of Content:

Slovenian

Mentor / Comentors:

Name and Surname	ID	Function
izr. prof. dr. Marko Robnik Šikonja	276	Mentor

Link to COBISS:

http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1536476611)

Institution:

University of Ljubljana

Department:

Faculty of Computer and Information Science

Item ID:

3052

Date Deposited:

08 Sep 2015 13:21

Last Modified:

17 Sep 2015 12:06

URI:

http://eprints.fri.uni-lj.si/id/eprint/3052

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