Utilizing the Adaptive Neural Network Algorithmic Approach for Analyzing English Text
Ming Feng Lu, Rongfa Chen · 2024
This study investigates the application of adaptive neural network algorithm models in English text analysis, emphasizing the enhancement of natural language processing (NLP) capabilities. By implementing fluency-based data augmentation, the research advances the creation of pseudo-parallel sentence pairs, which are vital for training models that perform translation, text correction, and content generation tasks with a proficiency comparable to native English speakers. The augmentation process relies on language models that evaluate the naturalness of sentences, ensuring semantic consistency across syntactic and lexical variations. A critical component of the research was examining the role of dropout as a regularization technique, which our analysis has shown to be instrumental in enhancing the performance of various optimization algorithms. The study compared models with and without dropout, revealing a substantial improvement in accuracy when dropout was utilized, thereby validating its efficacy in combating overfitting. The exploration of dynamic word vector technologies highlighted their significance in accurately processing polysemous language. Models like ELMo and BERT dynamically adapt word vectors to context, affording a nuanced understanding of language. Moreover, the study considered the balance between model complexity and efficiency, with architectures like ALBERT providing a leaner yet effective alternative to the standard BERT model. Furthermore, the research delved into decoding strategies, with a focus on Beam Search. This strategy's capacity for generating fluent and contextually appropriate text was assessed, alongside improvements such as random sampling and top-k sampling, which help to refine the search process within the probabilistic frameworks of language models. The research encapsulates the strides made in NLP through the application of adaptive neural network models. The integrated approach of data augmentation, regularization, dynamic word vectors, and strategic decoding has yielded models that showcase enhanced fluency and understanding of English text. These findings not only underscore the models' improved performance but also lay the groundwork for ongoing advancements in the field.