Parsing Words and Building Meanings: A Natural Language Processing Study

Abstract

This research paper explored natural language processing, word parsing, and meaning in terms of their implications for high-speed computing. Furthermore, it aimed to understand the connection between NLP and possibilities of language understanding in computers and how this knowledge can be applied to contemporary computer technology. The identified key barriers and solutions to NLP problems concerned the human inability to find the sequence and context notion that would frame even similarly disturbing behavior. The knowledge was enhanced by applying the theoretical framework of the distributional hypothesis and part-of-speech tagging in parsing methods for syntactic analysis pro and knowledge of hierarchical structure to maximize it. At the same time, it also addressed the method of increasing the number of performance of named entities per sentence paring them into several parts & ambiguity in the part of the speech to which a token belonged, and complexity in the previous named entity segmentation. The available data were obtained from newspapers & publishers: the editorials of newspapers and magazines, English imaginable sentences from Urdu newspapers like Edawn, and other public newspapers in Pakistan. A corpus of editorials was built through pre-processing published materials including tokenization, which involved distinguishing words and phrases, preparing a network of words substructure in semantic parsing pairs, and removing special tokens or brackets. The results show successful parsing & little sentential ambiguity & syntactic tree’s complexity. The high-profile result established human-computer communication possibilities at the next level and optimizations in NLP functionality for translation, information retrieval, and virtual assistant’s techniques, influencing notable theoretical contributions.