Urdu Dependency Parsing and Treebank Development: A Syntactic and Morphological Perspective
This work addresses parsing for Urdu, a low-resource language, but is incremental as it applies existing methods to new data.
The paper tackled dependency parsing for Urdu, a low-resource language with complex morphology, by developing a treebank and using feature models with Maltparser, achieving a labeled accuracy of 70% and unlabeled attachment score of 84%.
Parsing is the process of analyzing a sentence's syntactic structure by breaking it down into its grammatical components. and is critical for various linguistic applications. Urdu is a low-resource, free word-order language and exhibits complex morphology. Literature suggests that dependency parsing is well-suited for such languages. Our approach begins with a basic feature model encompassing word location, head word identification, and dependency relations, followed by a more advanced model integrating part-of-speech (POS) tags and morphological attributes (e.g., suffixes, gender). We manually annotated a corpus of news articles of varying complexity. Using Maltparser and the NivreEager algorithm, we achieved a best-labeled accuracy (LA) of 70% and an unlabeled attachment score (UAS) of 84%, demonstrating the feasibility of dependency parsing for Urdu.