一种面向问题分类的集成方法：融合 Electra Transformer、GloVe 与 LSTM
An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM
AI总结：
本文提出一种融合 Electra、GloVe 和 LSTM 的集成模型用于问题分类，在 TREC 数据集上取得 80% 准确率，优于 BERT、RoBERTa 和 DistilBERT 等基线模型。
摘要：
自然语言处理（NLP）已成为理解和生成人类语言的关键技术，在机器翻译、情感分析以及更密切相关的问题分类等任务中发挥着重要作用。作为 NLP 的一个子领域，问题分类专注于确定所寻求的信息类型，这是问答系统等下游应用的基础步骤。本研究提出了一种创新的问题分类集成方法，结合了 Electra、GloVe 和 LSTM 模型的优势。该模型在广受认可的 TREC 数据集上经过严格测试，展示了如何整合这些不同技术以获得更优结果。Electra 凭借其基于 Transformer 的能力实现复杂语言理解，GloVe 提供全局向量表示以捕捉词级语义，LSTM 则贡献其序列学习能力来建模长期依赖关系。通过策略性地融合这些要素，我们的集成模型为复杂的问题分类任务提供了稳健且高效的解决方案。通过与 BERT、RoBERTa 和 DistilBERT 等知名模型进行严格比较，该集成方法在测试数据集上取得了 80% 的准确率，验证了其有效性。
英文摘要：
Natural Language Processing (NLP) has emerged as a crucial technology for understanding and generating human language, playing an essential role in tasks such as machine translation, sentiment analysis, and more pertinently, question classification. As a subfield within NLP, question classification focuses on determining the type of information being sought, a fundamental step for downstream applications like question answering systems. This study presents an innovative ensemble approach for question classification, combining the strengths of Electra, GloVe, and LSTM models. Rigorously tested on the well-regarded TREC dataset, the model demonstrates how the integration of these disparate technologies can lead to superior results. Electra brings in its transformer-based capabilities for complex language understanding, GloVe offers global vector representations for capturing word-level semantics, and LSTM contributes its sequence learning abilities to model long-term dependencies. By fusing these elements strategically, our ensemble model delivers a robust and efficient solution for the complex task of question classification. Through rigorous comparisons with well-known models like BERT, RoBERTa, and DistilBERT, the ensemble approach verifies its effectiveness by attaining an 80% accuracy score on the test dataset.
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。
