Exploring Syntactic Patterns in Urdu: A Deep Dive into Dependency Analysis

• URL: http://arxiv.org/abs/2406.09549v1
• Date: Thu, 13 Jun 2024 19:30:32 GMT
• Title: Exploring Syntactic Patterns in Urdu: A Deep Dive into Dependency Analysis
• Authors: Nudrat Habib,
• Abstract summary: The dependency parsing approach is better suited for order-free languages like Urdu. The dependency tagset is designed after careful consideration of the complex morphological structure of the Urdu language.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parsing is the process of breaking a sentence into its grammatical components and identifying the syntactic structure of the sentence. The syntactically correct sentence structure is achieved by assigning grammatical labels to its constituents using lexicon and syntactic rules. In linguistics, parser is extremely useful due to the number of different applications like name entity recognition, QA systems and information extraction, etc. The two most common techniques used for parsing are phrase structure and dependency Structure. Because Urdu is a low-resource language, there has been little progress in building an Urdu parser. A comparison of several parsers revealed that the dependency parsing approach is better suited for order-free languages such as Urdu. We have made significant progress in parsing Urdu, a South Asian language with a complex morphology. For Urdu dependency parsing, a basic feature model consisting of word location, word head, and dependency relation is employed as a starting point, followed by more complex feature models. The dependency tagset is designed after careful consideration of the complex morphological structure of the Urdu language, word order variation, and lexical ambiguity and it contains 22 tags. Our dataset comprises of sentences from news articles, and we tried to include sentences of different complexity (which is quite challenging), to get reliable results. All experiments are performed using MaltParser, exploring all 9 algorithms and classifiers. We have achieved a 70 percent overall best-labeled accuracy (LA), as well as an 84 percent overall best-unlabeled attachment score (UAS) using the Nivreeager algorithm. The comparison of output data with treebank test data that has been manually parsed is then used to carry out error assessment and to identify the errors produced by the parser.

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