信息抽取中的分裂正确性
Split-Correctness in Information Extraction
摘要：
用于从文本中提取结构化信息的程序，即信息提取器，通常对从通用拆分操作获得的文档片段（例如句子、段落、k-gram、HTTP请求等）单独操作。将允许文本分析系统设计查询计划，对段进行并行评估，以加速大型文档的处理。其他应用包括对动态内容的增量评估，其中信息提取器的重新评估可以被限制为修改的片段，以及调试，其中信息提取器的开发人员被告知不同语义组件的潜在边界交叉。我们提出了一个新的形式框架内的文件spectrometry的形式主义的分裂正确性。我们的分析研究的复杂性，分裂正确性的定期spectrometry。我们还讨论了不同的变种分裂正确性，例如，在存在的黑盒提取器分裂约束。
英文摘要：
Programs for extracting structured information from text, namely information extractors, often operate separately on document segments obtained from a generic splitting operation such as sentences, paragraphs, k-grams, HTTP requests, and so on. An automated detection of this behavior of extractors, which we refer to as split-correctness, would allow text analysis systems to devise query plans with parallel evaluation on segments for accelerating the processing of large documents. Other applications include the incremental evaluation on dynamic content, where re-evaluation of information extractors can be restricted to revised segments, and debugging, where developers of information extractors are informed about potential boundary crossing of different semantic components. We propose a new formal framework for split-correctness within the formalism of document spanners. Our analysis studies the complexity of split-correctness over regular spanners. We also discuss different variants of split-correctness, for instance, in the presence of black-box extractors with split constraints.
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