Morphological segmentation of words is a subproblem of many natural language tasks, including handling out-of-vocabulary (OOV) words in machine translation, more effective information retrieval, and computer assisted vocabulary learning. Previous work typically relies on extensive statistical and semantic analyses to induce legitimate stems and affixes. We introduce a new learning based method and a prototype implementation of a knowledge light system for learning to segment a given word into word parts, including prefixes, suffixes, stems, and even roots. The method is based on the Conditional Random Fields (CRF) model. Evaluation results show that our method with a small set of seed training data and readily available resources can produce fine-grained morphological segmentation results that rival previous work and systems.
['Joseph Z. Chang', 'Jason S. Chang']
Word Root Finder: a Morphological Segmentor Based on CRF