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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.16846825 |
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Table of Contents:
- <p>In the era of Artificial Intelligence (AI), significant progress has been made by enabling machines to<br>understand and communicate in human languages. Central to this progress are parsers, which play a vital<br>role in syntactic analysis and support various Natural language Processing (NLP) applications, including<br>Machine Translation and sentiment analysis. This paper introduces a robust implementation of an<br>optimized Head-Driven Parser designed to advance NLP capabilities beyond the limitations of traditional<br>Lexicalized Tree Adjoining Grammar (L-TAG) based Parser. Traditional parser, while effective, often<br>struggle with the capturing complexities of natural languages, especially translation between English to<br>Indian languages. By leveraging Bi-directional approach and Head-Driven techniques, this research offers<br>a revolutionary enhancement in parsing frameworks. This method not only improves performance in<br>syntactic analysis but also facilitates complex tasks such as discourse analysis and semantic parsing. This<br>research involves experimentation the Bi-Directional Parser on a dataset of 15,000 sentences, resulting a<br>reduction in derivation variations compared to conventional TAG Parsers. This advancement highlights<br>how Head-Driven Parsing can overcome traditional constraints and provide more reliable linguistic<br>analysis. The paper demonstrates how this new implementation not only builds on the strengths of L-TAG<br>but also addresses its limitations and contributes to expanding the scope of Tree Adjoining Grammarbased methodologies and advancing the field of Machine Translation.</p>