📄 中文摘要
在现代数据处理管道中，语义分析的方式多种多样。许多开发者开始探索使用大型语言模型（LLMs）来增强语义理解能力，以便更好地处理复杂的文本数据。这些模型能够捕捉上下文信息，提供更深层次的语义分析。然而，传统的自然语言处理技术仍然在某些场景中发挥着重要作用，尤其是在资源有限或对实时性要求较高的情况下。通过对比这两种方法的优缺点，开发者可以选择最适合其特定需求的解决方案。
你在管道中如何进行语义分析？是使用大型语言模型进行丰富，还是坚持传统的自然语言处理？
📄 English Summary
How do you approach semantic analysis in your pipelines? Are you using LLMs for enrichment, or sticking with traditional NLP? I'd love to hear what's working (and what's not).
Semantic analysis in modern data processing pipelines can be approached in various ways. Many developers are exploring the use of Large Language Models (LLMs) to enhance semantic understanding, allowing for better handling of complex text data. These models can capture contextual information and provide deeper semantic insights. However, traditional Natural Language Processing (NLP) techniques still play a crucial role in certain scenarios, especially when resources are limited or real-time processing is required. By comparing the advantages and disadvantages of these two approaches, developers can select the most suitable solution for their specific needs.