# A Morphology-aware Network for Morphological Disambiguation

## TL;DR

This paper introduces a deep learning-based system for morphological disambiguation in agglutinative languages, achieving high accuracy without language-specific features, and demonstrates its effectiveness on Turkish, French, and German.

## Contribution

The paper presents a novel morphology-aware neural network architecture that performs high-accuracy morphological disambiguation across multiple languages without language-specific engineering.

## Key findings

- Achieved 84.12% accuracy for Turkish
- Achieved 88.35% accuracy for German
- Achieved 93.78% accuracy for French

## Abstract

Agglutinative languages such as Turkish, Finnish and Hungarian require morphological disambiguation before further processing due to the complex morphology of words. A morphological disambiguator is used to select the correct morphological analysis of a word. Morphological disambiguation is important because it generally is one of the first steps of natural language processing and its performance affects subsequent analyses. In this paper, we propose a system that uses deep learning techniques for morphological disambiguation. Many of the state-of-the-art results in computer vision, speech recognition and natural language processing have been obtained through deep learning models. However, applying deep learning techniques to morphologically rich languages is not well studied. In this work, while we focus on Turkish morphological disambiguation we also present results for French and German in order to show that the proposed architecture achieves high accuracy with no language-specific feature engineering or additional resource. In the experiments, we achieve 84.12, 88.35 and 93.78 morphological disambiguation accuracy among the ambiguous words for Turkish, German and French respectively.