English

Urdu Dependency Parsing and Treebank Development: A Syntactic and Morphological Perspective

Computation and Language 2024-10-03 v2 Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2406.09549,
  title  = {Urdu Dependency Parsing and Treebank Development: A Syntactic and Morphological Perspective},
  author = {Nudrat Habib},
  journal= {arXiv preprint arXiv:2406.09549},
  year   = {2024}
}
R2 v1 2026-06-28T17:05:15.546Z