Serdar KOCAK, Yusuf Tansel İÇ, Mustafa SERT, Berna DENGİZ

Ar-Ge projelerinin sınıflandırılması için doğal Türkçe dil işleme tabanlı yöntem

Birçok farklı sektörde metin halinde bulunan verilerden istenilen bilgilerin elde edilmesi için doğal dil işleme, metin madenciliği ve derin öğrenme yöntemleri kullanılmaktadır. Son zamanlarda artan Ar-Ge proje sayıları ve farklılaşan proje faaliyet alanları ile birlikte Ar-Ge projelerinin ait olduğu araştırma alanlarının belirlenmesi ve bu araştırma alanlarına uygun hakemlerin tespitinde yaşanan sıkıntılar nedeniyle projelerin desteklenme süreçleri olumsuz etkilenebilmektedir. Bu makalede, Ar-Ge projelerinin sınıflandırılması amacıyla öncelikli olarak çalışmanın gerçekleştirildiği veri tabanındaki veriler temizlenmiş ve doğal dil tekniklerinden biri olan “Word2Vec” kelime temsili yöntemi ile otomatik özellik öğrenme yaklaşımı kullanılarak özelliklerin sınıflandırılması amacıyla Evrişimsel Sinir Ağları (CNN-Convolutional Neural Network) modelleri oluşturulmaya çalışılmıştır. TUBİTAK Dergipark sitesinden seçilen ve sınıfları belli olan Ar-Ge projeleri ve Ar-Ge proje içeriğine sahip makalelerden oluşan veri kümesi üzerinde yapılan deneysel çalışmalardan elde edilen değerlendirme sonuçları ile diğer klasik algoritmalar karşılaştırılmış ve özellikle Word2Vec modellerine sahip CNN’lerin daha etkili sonuçları ürettiği birçok performans parametresi ile gösterilmiştir.

Anahtar Kelimeler:

Metin sınıflandırma, doğal dil işleme, proje seçimi, evrişimsel sinir ağları, word2vec

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Türkiye Bilimsel Ve Teknolojik Araştırma Kurumu (TÜBİTAK) Faaliyet Raporları (2019), https://www.tubitak.gov.tr/sites/default/files/18842/tubitak_2019_yili_faaliyet_raporu.pdf sayfasından erişilmiştir, 2019.
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