Arabic Word Segmentation With Long Short-Term Memory Neural Networks and Word Embedding

Abstract

In this paper, we propose an Arabic word segmentation technique based on a bi-directional long short-term memory deep neural network. This paper addresses the two tasks of word segmentation only and word segmentation for nine cases of the rewrite. Word segmentation with a rewrite concerns inferring letters that are dropped or changed when the main word unit is attached to another unit, and it writes these letters back when the two units are separated as a result of segmentation. We only use binary labels as indicators of segmentation positions. Therefore, label 1 is an indicator of the start of a new word (split) in a sequence of symbols not including whitespace, and label 0 is an indicator for any other case (no-split). This is different from the mainstream feature representation for word segmentation in which multi-valued labeling is used to mark the sequence symbols: beginning, inside, and outside. We used the Arabic Treebank data and its clitics segmentation scheme in our experiments. The trained model without the help of any additional language resources, such as dictionaries, morphological analyzers, or rules, achieved a high F1 value for the Arabic word segmentation only (98.03%) and Arabic word segmentation with the rewrite (more than 99% for frequent rewrite cases). We also compared our model with four state-of-the-art Arabic word segmenters. It performed better than the other segmenters on a modern standard Arabic text, and it was the best among the segmenters that do not use any additional language resources in another test using classical Arabic text.