A Knowledge-Poor Approach to Turkish Text Categorization
Küçük Resim Yok
Tarih
2014
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer-Verlag Berlin
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Document categorization is a way of determining a category for a given document. Supervised methods mostly rely on a training data and rich linguistic resources that are either language-specific or generic. This study proposes a knowledge-poor approach to text categorization without using any sets of rules or language specific resources such as part-of-speech tagger or shallow parser. Knowledge-poor here refers to lack of a reasonable amount of background knowledge. The proposed system architecture takes data as-is and simply separates tokens by space. Documents represented in vector space models are used as training data for many machine learning algorithm. We empirically examined and compared a several factors from similarity metrics to learning algorithms in a variety of experimental setups. Although researchers believe that some particular classifiers or metrics are better than others for text categorization, the recent studies disclose that the ranking of the models purely depends on the class, experimental setup and domain as well. The study features extensive evaluation, comparison within a variety of experiments. We evaluate models and similarity metrics for Turkish language as one of the agglutinative language especially within poor-knowledge framework. It is seen that output of the study would be very beneficial for other studies.
Açıklama
15th Annual Conference on Intelligent Text Processing and Computational Linguistics (CICLing) -- APR 06-12, 2014 -- Ctr Commun & Dev, Kathmandu, NEPAL
Anahtar Kelimeler
Text Categorization, Vector Space Model, Machine Learning
Kaynak
Computational Linguistics and Intelligent Text Processing, Cicling 2014, Part Ii
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
8404
