文档生成器的语法

摘要：

我们提出了一种新的基于语法的语言，用于定义信息提取器的文件（文本），这是建立在良好的研究框架的文件spectrometry从文本中提取结构化数据。虽然以前研究的形式主义的文档spectrum主要是基于正则表达式，我们使用的扩展上下文无关文法，称为{提取语法}，定义新的类上下文无关spectrum。提取语法是简单的上下文无关文法，扩展了捕获文档的间隔位置（即span）的变量。正则表达式对于标记和标记是有效的，而上下文无关文法对于捕获结构属性也是有效的。事实上，我们表明，上下文无关的sparkle严格比他们的常规同行更有表现力。我们的原因，我们的新类的表达能力，并提出了一个下推自动机模型，捕捉它。我们表明，提取语法可以评估多项式的数据复杂度。然而，由于多项式的程度取决于查询，我们提出了一个枚举算法的明确提取语法，五次预处理后，输出结果顺序，没有重复，每两个连续的之间有一个恒定的延迟。

英文摘要：

We propose a new grammar-based language for defining information-extractors from documents (text) that is built upon the well-studied framework of document spanners for extracting structured data from text. While previously studied formalisms for document spanners are mainly based on regular expressions, we use an extension of context-free grammars, called {extraction grammars}, to define the new class of context-free spanners. Extraction grammars are simply context-free grammars extended with variables that capture interval positions of the document, namely spans. While regular expressions are efficient for tokenizing and tagging, context-free grammars are also efficient for capturing structural properties. Indeed, we show that context-free spanners are strictly more expressive than their regular counterparts. We reason about the expressive power of our new class and present a pushdown-automata model that captures it. We show that extraction grammars can be evaluated with polynomial data complexity. Nevertheless, as the degree of the polynomial depends on the query, we present an enumeration algorithm for unambiguous extraction grammars that, after quintic preprocessing, outputs the results sequentially, without repetitions, with a constant delay between every two consecutive ones.

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。