An investigation of computational models for syntactic parsing in Igbo language texts
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- NGN 5000
Background of the Study
Syntactic parsing is a crucial component of natural language processing, enabling the structural analysis of sentences to reveal grammatical relationships. For Igbo language texts, which possess unique syntactic structures and flexible word order, computational models must be specifically adapted to capture these nuances. Recent advancements in deep learning and probabilistic parsing techniques have shown promise in automating syntactic analysis for resource-rich languages; however, Igbo remains under-resourced (Nwachukwu, 2023). Research efforts have focused on developing rule-based, statistical, and hybrid models that can effectively parse Igbo sentences by accounting for its morphological markers and syntactic variability. Studies (Chukwu, 2024) indicate that integrating linguistic theory with machine learning algorithms can improve parsing accuracy. Nevertheless, challenges persist due to limited annotated corpora and the informal nature of many Igbo texts. The growing availability of digital texts in Igbo offers an opportunity to refine computational models further. This investigation aims to assess current computational models for syntactic parsing in Igbo language texts, identify inherent limitations, and propose enhancements to better accommodate Igbo’s grammatical complexities.
Statement of the Problem
Although computational models for syntactic parsing have advanced, existing approaches often underperform when applied to Igbo language texts. The unique syntactic constructions, free word order, and limited availability of annotated Igbo corpora contribute to parsing errors and reduced accuracy (Nwachukwu, 2023; Chukwu, 2024). These shortcomings hinder the development of effective NLP applications such as machine translation and information extraction. Furthermore, many models struggle to accommodate the variability found in informal and literary Igbo texts. There is a need for a systematic investigation to identify the specific challenges faced by current syntactic parsers and to propose tailored modifications that address the linguistic peculiarities of Igbo.
Objectives of the Study
- To evaluate the performance of existing computational models for syntactic parsing in Igbo texts.
- To identify linguistic challenges specific to Igbo syntax that impact parsing accuracy.
- To recommend model enhancements and data augmentation strategies to improve parsing performance.
Research Questions
- How effective are current models in parsing Igbo language texts?
- What syntactic challenges unique to Igbo contribute to parsing errors?
- How can computational models be improved to better accommodate Igbo grammatical structures?
Significance of the Study
This study is significant as it advances the field of computational linguistics by focusing on syntactic parsing for the Igbo language. Enhanced parsing models will facilitate improved NLP applications such as translation, summarization, and information retrieval for Igbo texts. The findings will benefit linguists, developers, and educators by providing insights into Igbo syntax and guiding future research on under-resourced languages. Ultimately, this work contributes to the preservation and technological empowerment of the Igbo language.
Scope and Limitations of the Study
This study focuses exclusively on computational models for syntactic parsing in Igbo language texts. It does not cover semantic analysis or languages outside Igbo.
Definitions of Terms
- Syntactic Parsing: The process of analyzing the grammatical structure of sentences.
- Computational Models: Algorithmic frameworks designed to process and analyze language data.
- Igbo Language: A major indigenous language spoken in southeastern Nigeria.
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