An Unsupervised Method for Uncovering Morphological Chains
This addresses the limitation of existing systems that rely only on orthographic patterns, offering a more comprehensive approach for linguistic analysis in multiple languages.
The paper tackled the problem of unsupervised morphological analysis by integrating orthographic and semantic views to model morphological chains, resulting in a model that consistently matches or outperforms five state-of-the-art systems on Arabic, English, and Turkish.
Most state-of-the-art systems today produce morphological analysis based only on orthographic patterns. In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words. We model word formation in terms of morphological chains, from base words to the observed words, breaking the chains into parent-child relations. We use log-linear models with morpheme and word-level features to predict possible parents, including their modifications, for each word. The limited set of candidate parents for each word render contrastive estimation feasible. Our model consistently matches or outperforms five state-of-the-art systems on Arabic, English and Turkish.