The Transition from Symbolic NLP to Intuitive NLP: Addressing Ambiguity and Parsing Challenges

The advancement of Natural Language Processing (NLP) from traditional rule-based methods to modern deep learning-driven approaches has significantly improved the ability to handle linguistic ambiguity and enhance parsing accuracy. While symbolic methods offer interpretability, they often lack scalability and adaptability to diverse linguistic contexts. On the other hand, deep learning techniques, particularly transformer-based models such as BERT, have demonstrated exceptional performance in analyzing complex linguistic structures, minimizing ambiguity, and achieving high accuracy in parsing tasks. This research examines the various challenges associated with lexical, syntactic, semantic, and pragmatic ambiguities in NLP applications and explores strategies to address them. Techniques such as Word Sense Disambiguation (WSD), Named Entity Recognition (NER), dependency parsing, and probabilistic models have played a crucial role in improving ambiguity resolution. Additionally, a comparative analysis of parsing models highlights the significance of evaluating their precision, computational efficiency, and adaptability across different domains.

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