| Yazarlar |
Dr. Öğr. Üyesi Ayla KAYABAŞ
Kırşehir Ahi Evran Üniversitesi, Türkiye |
Ahmet Ercan Topcu
Türkiye |
|
Özkan Kılıç
Türkiye |
| Özet |
| In this study, we present a novel algorithm that combines a rule-based approach and an artificial neural network-based approach in morphological analysis. The usage of hybrid models including both techniques is evaluated for performance improvements. The proposed hybrid algorithm is based on the idea of the dynamic generation of an artificial neural network according to two-level phonological rules. In this study, the combination of linguistic parsing, a neural network-based error correction model, and statistical filtering is utilized to increase the coverage of pure morphological analysis. We experimented hybrid algorithm applying rule-based and long short-term memory-based (LSTM-based) techniques, and the results show that we improved the morphological analysis performance for optical character recognizer (OCR) and social media data. Thus, for the new hybrid algorithm with LSTM, the accuracy reached 99.91% for the OCR dataset and 99.82% for social media data. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | Turkish Journal of Electrical Engineering and Computer Sciences |
| Dergi ISSN | 1300-0632 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2022 |
| Doi Numarası | 10.55730/1300-0632.3901 |
| Makale Linki | https://journals.tubitak.gov.tr/elektrik/vol30/iss5/4/ |
| Atıf Sayıları | |
| TRDizin | 1 |
