A novel hybrid algorithm for morphological analysis: artificial Neural-Net-XMOR

Authors

AYLA KAYABAŞ
AHMET ERCAN TOPCU
ÖZKAN KILIÇ

DOI

10.55730/1300-0632.3901

Abstract

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.

Keywords

Algorithm, artificial neural network, rule-based system, morphology, hybrid model

First Page

1726

Last Page

1740

Recommended Citation

KAYABAŞ, AYLA; TOPCU, AHMET ERCAN; and KILIÇ, ÖZKAN (2022) "A novel hybrid algorithm for morphological analysis: artificial Neural-Net-XMOR," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 30: No. 5, Article 4. https://doi.org/10.55730/1300-0632.3901
Available at: https://journals.tubitak.gov.tr/elektrik/vol30/iss5/4