基于Transformer的先进语言模型演进分析：观点挖掘实验
Analysis of the Evolution of Advanced Transformer-Based Language Models: Experiments on Opinion Mining
AI总结：
本文通过实验分析先进Transformer语言模型在观点挖掘中的表现，并进行高层次比较，为工程师选型和研究者后续方向提供指导。
摘要：
观点挖掘，又称情感分析，是自然语言处理（NLP）的一个子领域，专注于识别和提取文本材料中的主观信息。这可以包括确定一段文本的整体情感（例如积极或消极），以及识别文本中表达的具体情绪或观点，其中涉及使用先进的机器学习和深度学习技术。近年来，基于Transformer的语言模型凭借注意力机制和并行计算，使得这一人类情感分析任务变得直观易行。与循环神经网络不同，这些优势使此类模型在语言任务上非常强大，而循环神经网络在顺序处理上花费大量时间，导致其在处理长文本时容易失败。本文的研究范围旨在研究最先进的基于Transformer的语言模型在观点挖掘上的行为，并提供它们之间的高层次比较，以突出其关键特性。此外，我们的比较研究为生产工程师指明了应关注的方法方向并铺平了道路，同时为研究人员提供了未来研究课题的指导方针。
英文摘要：
Opinion mining, also known as sentiment analysis, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information in textual material. This can include determining the overall sentiment of a piece of text (e.g., positive or negative), as well as identifying specific emotions or opinions expressed in the text, that involves the use of advanced machine and deep learning techniques. Recently, transformer-based language models make this task of human emotion analysis intuitive, thanks to the attention mechanism and parallel computation. These advantages make such models very powerful on linguistic tasks, unlike recurrent neural networks that spend a lot of time on sequential processing, making them prone to fail when it comes to processing long text. The scope of our paper aims to study the behaviour of the cutting-edge Transformer-based language models on opinion mining and provide a high-level comparison between them to highlight their key particularities. Additionally, our comparative study shows leads and paves the way for production engineers regarding the approach to focus on and is useful for researchers as it provides guidelines for future research subjects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
