Pavan Kurariya, Prashant Chaudhary, Jahnavi Bodhankar, Lenali Singh and Ajai Kumar, Centre for Development of Advanced Computing, India

In the age of Artificial Intelligence (AI), a significant breakthrough occurred as machines demonstrated their ability to communicate in human languages. This marked the beginning of a ground-breaking era in Natural Language Processing., defined by unparalleled computational capabilities. Amidst this evolution, parsers stand as an indispensable component, facilitating syntactic comprehension and empowering various NLP applications, from Machine Translation to sentiment analysis. Parser plays a crucial role in deciphering the complex syntactic structures inherent in human languages. With the use of a parser, machines can comprehend human language, extract meaning, and facilitate a variety of natural language processing (NLP) applications, such as information retrieval, sentiment analysis, and machine translation. This research paper presents the implementation of Bi-Directional Head-Driven Parser, aiming to expand the horizons of NLP beyond the constraints of traditional early-type L-TAG (Lexicalized Tree Adjoining Grammar) Parsing. While effective, conventional Parsers encounter inherent limitations in grappling with the intricacies and subtleties of natural language. Through the utilization of Bi-Directional principles, Head-Driven techniques offer a revolutionary breakthrough in computational frameworks for large-scale grammar parsing, enabling complex NLP tasks such as discourse analysis and semantic parsing, and guaranteeing reliable linguistic analysis for practical applications. The performance of the Bi-Directional Parser has been examined on the data set of 15000 sentences and observed a reduction in the variation of derivations for sentences of the same length compared to the conventional TAG Parser, this research showcases how Head-Driven Parser facilitates breakthrough in language processing, syntactic analysis, semantic comprehension, and beyond. Moreover, it underscores the structural implications of integrating Head-Driven Parsing. Traditional approaches, such as Tree Adjoining Grammar (TAG), while valuable, often encounter limitations in capturing the full spectrum of linguistic phenomena, particularly in the context of cross-linguistic transfer between English and Indian languages. In light of the significance of natural language processing (NLP) in addressing these issues, this research introduces a Bi-Directional Head-Driven Parser implementation. Drawing upon the rich foundation of TAG and acknowledging its constraints, our approach transcends these limitations by harnessing advanced parsing traversal techniques and linguistic theories. By bridging the gap between theory and application, our approach not only enhances our understanding of syntactic parsing across language families but also surpasses the performance of an 'Early-type Parser' in terms of time and memory. Through rigorous experimentation and evaluation, this research contributes to the ongoing discourse on expanding the frontiers of Tree Adjoining Grammar-based research and shaping the trajectory of Machine Translation

Artificial intelligence (AI), Natural Language Processing (NLP), Tree Adjoining Grammar (TAG), L-TAG (Lexicalized Tree Adjoining Grammar)