Summary of Urdu Dependency Parsing and Treebank Development: a Syntactic and Morphological Perspective, by Nudrat Habib
Urdu Dependency Parsing and Treebank Development: A Syntactic and Morphological Perspective
by Nudrat Habib
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach to dependency parsing for Urdu language involves two models: a basic feature model and an advanced model that incorporates part-of-speech tags and morphological attributes. The basic model focuses on word location, head word identification, and dependency relations, while the advanced model adds POS tags and morphological features such as suffixes and gender. The researchers manually annotated a corpus of news articles with varying complexity and achieved a best-labeled accuracy (LA) of 70% and an unlabeled attachment score (UAS) of 84%. This demonstrates the feasibility of dependency parsing for Urdu, which is critical for various linguistic applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dependency parsing helps understand sentence structure. For Urdu language, this is important because it has complex words and no fixed word order. The researchers created two models to analyze sentences. One model looks at word position, head words, and relationships between them. The other model adds more details like part-of-speech tags and word endings. They tested their approach on news articles and got good results. |
Keywords
* Artificial intelligence * Dependency parsing
