Preparing Interdisciplinary Graduate Course Contents Using Natural Language Processing Techniques

Year 2020, , 373 - 383, 30.10.2020

Ahmet Albayrak

https://doi.org/10.17671/gazibtd.714447

Cited By: 4

Abstract

In this study, natural language processing methods, one of the data mining techniques, were used to prepare the content of an interdisciplinary course that is planned to be opened at graduate level. The graduate course is called Data Science and Applications. Data science is an interdisciplinary concept that includes statistics and computer science. The course has no place in the literature with a similar name. Data science is an approach that prioritizes data and is applied in many fields. Since the application area is very wide, the course is called Data Science and Applications. Papers published at a conference organized by IEEE for years were used as a data set in determining the course content. The conference called Data Science and Advanced Analytics will be held for the 7th time this year. Papers accepted to the conference in 2015, 2016, 2017 and 2018 were used in the data set. The title texts and keywords of the papers were analyzed with natural language processing techniques and the course content was determined. In this study, after the first data set was prepared, data-cleaning process was performed on the data, and then the title of the papers was divided into words. The frequencies of the words are found in the data set devoted to the words and the first twenty words are selected according to the frequency. Apache Spark NTK package was used in the natural language processing process. Since the 20 words chosen are atomic, the main topic titles are determined by the induction method.

Keywords

data science, natural language processing, course content preparation, data scientist, topic modeling

Doğal Dil İşleme Teknikleri Kullanılarak Disiplinler Arası Lisansüstü Ders İçeriği Hazırlanması

Abstract

Bu çalışmada lisansüstü seviyede açılan düşünülen disiplinler arası bir dersin içeriğinin hazırlanması için veri madenciliği tekniklerinden doğal dil işleme yöntemleri kullanılmıştır. Lisansüstü ders, Veri Bilimi ve Uygulamaları adını taşımaktadır. Veri bilimi temelde istatistik ve bilgisayar bilimlerini içine alan disiplinler arası bir kavramdır. Dersin benzer bir ad ile literatürde yeri yoktur. Veri bilimi yaklaşımı veriyi öncelikleyen ve oldukça fazla alanda uygulanan bir yaklaşımdır. Uygulama alanı çok geniş olduğundan derse Veri Bilimi ve Uygulamaları adı verilmiştir. IEEE’nin yıllardır düzenlediği bir konferansta basılan bildiriler ders içeriğinin belirlenmesinde veri seti olarak kullanılmıştır. Data Science and Advanced Analytics adındaki konferansın bu yıl 7. si düzenlenecektir. 2015, 2016, 2017 ve 2018 yıllarında konferansa kabul edilen bildiriler veri setinde kullanılmıştır. Bildirilerin başlık kısımları ve anahtar kelimeler doğal dil işleme teknikleri ile analiz edilerek ders içeriği belirlenmiştir. Bu çalışmada ilk olarak veri seti hazırlandıktan sonra, veri üzerinde veri temizleme işlemi yapılmış ardından bildiri başlıkları sözcüklere ayrılmıştır. Sözcüklere ayrılan veri seti içinde sözcüklerin frekansları bulunarak frekansa göre ilk yirmi sözcük seçilmiştir. Doğal dil işleme sürecinde Apache Spark NTK paketi kullanılmıştır. Seçilen 20 sözcük atomik olduğundan tümevarım yöntemi ile ana konu başlıkları belirlenmiştir.

Keywords

veri bilimi, doğal dil işleme, ders içeriği hazırlama, veri bilimcisi, konu modelleme

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