Topic and Sub-Topic Detection Model in English Documents

Yıl 2018, , 754 - 764, 01.08.2018

Metin Turan , Sena Ögtelik

https://doi.org/10.29130/dubited.420104

Öz

In this article, a model of topic and sub topic detection is proposed in the documents and experimental findings are evaluated. The Gestalt theory based on the Helmholtz principle was used in the documents to determine the meaningful words that could be used to determine concepts and sub topic. An Artificial Neural Network (ANN) model was established in which these words were entered, and this network was trained with number of 140 training documents. The training and testing document dataset is about the sports and training topics and 14 subtopics have been selected. The output of ANN gives the topic and sub topic information. Experiments were executed with 70 test documents with different numbers of (5, 10, 20) words. It was observed that the success rate was approximately 95% in the topic and 80% in the sub topic.

Anahtar Kelimeler

Natural Language Processing, Artificial Neural Networks, Helmholtz Principle, Classification

İngilizce Dokümanlarda Tema ve Alt Kavramlar Tespit Modeli

Öz

Bu makalede
dokümanlarda tema ve alt kavram tespiti konusunda bir model önerilmiş ve
deneysel bulgular değerlendirilmiştir. Dokümanlarda tema ve alt kavramların
tespiti için kullanılabilecek anlamlı sözcüklerin belirlenmesi amacıyla Helmholtz
prensibi temelli Gestalt teorisi kullanılmıştır. Bu sözcüklerin girdi olduğu
bir Yapay Sinir Ağı (YSA) modeli oluşturulmuş, eğitim dokümanları (140 adet)
ile bu ağ eğitilmiştir. Eğitim ve sınama doküman veri seti spor ve eğitim
temalarında olup, toplam 14 alt kavram seçilmiştir. YSA’nın çıktısı tema ve
alt-kavram bilgilerini vermektedir. 70 adet sınama dokümanı ile farklı sayıda
(5, 10, 20) anlamlı kelime seçilerek deneyler yapılmış, başarı oranının
konularda yaklaşık olarak %95, alt kavramlarda ise %80 olduğu gözlemlenmiştir.

Anahtar Kelimeler

Doğal Dil İşleme, Yapay Sinir Ağları, Helmholtz Prensibi

Kaynakça

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