Language Model Based Arabic Word Segmentation 
 We approximate Arabic's rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme ). Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus . The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input . The language model is initially estimated from a small manually segmented corpus of about 110,000 words . To improve the segmentation accuracy , we use an unsupervised algorithm for automatically acquiring new stems from a 155 million word unsegmented corpus , and re-estimate the model parameters with the expanded vocabulary and training corpus . The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens . We believe this is a state-of-the-art performance and the algorithm can be used for many highly inflected languages provided that one can create a small manually segmented corpus of the language of interest. 