Information Extraction Algorithms and Semantic Understanding in Natural Language Processing

Complex natural language texts affect the accuracy of information extraction and semantic understanding. To address this problem, this paper applies the attention mechanism to make full use of contextual information to solve the problems of ambiguity and contextual understanding. Deep learning technology is used to process complex syntactic structures. This paper explores how to apply the theory of information extraction and semantic understanding to various practical problems, and provides new ideas for improving the efficiency and quality of natural language processing. Through this study, the accuracy, robustness and accuracy of information extraction and semantic understanding have all been improved, with the highest valuesof the three indicators being $95.9 \%, \quad 0.90 \%$ and $97.9 \%$ respectively.