Evaluating the machine learning models based on natural language processing tasks

Meeradevi Meeradevi, S. B. J., Swetha B. N.
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Abstract

In the realm of natural language processing (NLP), a diverse array of language models has emerged, catering to a wide spectrum of tasks, ranging from speaker recognition and auto-correction to sentiment analysis and stock prediction. The significance of language models in enabling the execution of these NLP tasks cannot be overstated. This study proposes an approach to enhance accuracy by leveraging a hybrid language model, combining the strengths of long short-term memory (LSTM) and gated recurrent unit (GRU). LSTM excels in preserving long-term dependencies in data, while GRU's simpler gating mechanism expedites the training process. The research endeavors to evaluate four variations of this hybrid model: LSTM, GRU, bidirectional long short-term memory (Bi-LSTM), and a combination of LSTM with GRU. These models are subjected to rigorous testing on two distinct datasets: one focused on IBM stock price prediction, and the other on Jigsaw toxic comment classification (sentiment analysis). This work represents a significant stride towards democratizing NLP capabilities, ensuring that even in resource-constrained settings, NLP models can exhibit improved performance. The anticipated implications of these findings span a wide spectrum of real-world applications and hold the potential to stimulate further research in the field of NLP. 
根据自然语言处理任务评估机器学习模型
在自然语言处理(NLP)领域,出现了各种各样的语言模型,可满足从说话人识别和自动纠错到情感分析和股票预测等各种任务。语言模型在执行这些 NLP 任务中的重要性无论怎样强调都不为过。本研究提出了一种利用混合语言模型提高准确性的方法,它结合了长短期记忆(LSTM)和门控递归单元(GRU)的优势。LSTM 擅长保存数据中的长期依赖关系,而 GRU 更简单的门控机制则加快了训练过程。本研究致力于评估这种混合模型的四种变体:LSTM、GRU、双向长短期记忆(Bi-LSTM)以及 LSTM 与 GRU 的组合。这些模型在两个不同的数据集上进行了严格测试:一个侧重于 IBM 股票价格预测,另一个侧重于 Jigsaw 有毒评论分类(情感分析)。这项工作在实现 NLP 能力民主化方面迈出了重要一步,确保了即使在资源有限的情况下,NLP 模型也能表现出更高的性能。这些发现的预期影响涵盖了现实世界的广泛应用,并有可能促进 NLP 领域的进一步研究。
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