SpannerLib：在命令式工作流中嵌入声明式信息抽取
SpannerLib: Embedding Declarative Information Extraction in an Imperative Workflow
AI总结：
SpannerLib是一个在Python中嵌入文档spanner的库，通过实现Spannerlog实现规则嵌入与自定义Python函数调用，并在Jupyter Notebook中展示复杂度递增的信息抽取程序。
摘要：
文档spanner已被提出作为一种从文本中进行声明式信息抽取（IE）的形式化框架，其追随了工业界和学术界的信息抽取产品。在过去十年中，该框架在表达能力、复杂性以及自然结合文本分析与关系查询的能力方面得到了深入研究。本演示展示了SpannerLib，一个用于在Python代码中嵌入文档spanner的库。SpannerLib通过提供Spannerlog（基于Datalog的文档spanner）的实现来促进信息抽取程序的开发，该实现以两个方向与Python代码交互：规则可以嵌入在Python内部，并且它们可以通过用户自定义函数调用自定义的Python代码（例如，对基于机器学习的NLP模型的调用）。演示场景在Jupyter Notebook中展示了复杂度逐渐增加的信息抽取程序。
英文摘要：
Document spanners have been proposed as a formal framework for declarative Information Extraction (IE) from text, following IE products from the industry and academia. Over the past decade, the framework has been studied thoroughly in terms of expressive power, complexity, and the ability to naturally combine text analysis with relational querying. This demonstration presents SpannerLib a library for embedding document spanners in Python code. SpannerLib facilitates the development of IE programs by providing an implementation of Spannerlog (Datalog-based documentspanners) that interacts with the Python code in two directions: rules can be embedded inside Python, and they can invoke custom Python code (e.g., calls to ML-based NLP models) via user-defined functions. The demonstration scenarios showcase IE programs, with increasing levels of complexity, within Jupyter Notebook.
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。
