Urdu Dependency Parsing and Treebank Development: A Syntactic and Morphological Perspective
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909332093796352 |
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| author | Habib, Nudrat |
| author_facet | Habib, Nudrat |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_09549 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Urdu Dependency Parsing and Treebank Development: A Syntactic and Morphological Perspective Habib, Nudrat Computation and Language Machine Learning 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. |
| title | Urdu Dependency Parsing and Treebank Development: A Syntactic and Morphological Perspective |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2406.09549 |