Year: 2015
Type: conference-paper
Abstract: Arabic, Hebrew, and similar languages are typically written without diacritics, leading to ambiguity and posing a major challenge for core language processing tasks like speech recognition.Previous approaches to automatic diacritization employed a variety of machine learning techniques.However, they typically rely on existing tools like morphological analyzers and therefore cannot be easily extended to new genres and languages.We develop a recurrent neural network with long shortterm memory layers for predicting diacritics in Arabic text.Our language-independent approach is trained solely from diacritized text without relying on external tools.We show experimentally that our model can rival state-of-the-art methods that have access to additional resources.
DOI: 10.18653/v1/d15-1274
Authors: Yonatan Belinkov, James Glass
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2250751111