综述NLP驱动的信息检索新方法，涵盖模型、工具与应用。
Exploring new Approaches for Information Retrieval through Natural Language Processing
- 对比稀疏、稠密与混合检索方法，分析其适用场景。
- 总结BERT等预训练模型在搜索精度上的提升效果。
- 适合关注搜索算法与NLP融合的研究者参考。
本文综述了自然语言处理在信息检索领域的最新进展与新兴方法。回顾了布尔模型、向量空间模型、概率模型和推理网络模型等传统检索方法，并重点介绍深度学习、强化学习以及BERT等预训练变换器模型的现代技术。讨论了Lucene、Anserini和Pyserini等关键工具与库在高效文本索引与搜索中的应用。对稀疏、稠密及混合检索方法进行了比较分析，并探讨了其在网页搜索引擎、跨语言信息检索、论据挖掘、隐私信息检索和仇恨言论检测中的实际应用。最后，指出现有挑战与未来研究方向，包括提升检索准确性、可扩展性及伦理考量。
原文摘要 · Abstract (English)
This review paper explores recent advancements and emerging approaches in Information Retrieval (IR) applied to Natural Language Processing (NLP). We examine traditional IR models such as Boolean, vector space, probabilistic, and inference network models, and highlight modern techniques including deep learning, reinforcement learning, and pretrained transformer models like BERT. We discuss key tools and libraries - Lucene, Anserini, and Pyserini - for efficient text indexing and search. A comparative analysis of sparse, dense, and hybrid retrieval methods is presented, along with applications in web search engines, cross-language IR, argument mining, private information retrieval, and hate speech detection. Finally, we identify open challenges and future research directions to enhance retrieval accuracy, scalability, and ethical considerations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。