Scalable Information Extraction in Stratosphere

该项目的主要目标是实现对大量非结构化文本的基于查询的分析。我们设想用户制定IE任务与同温层查询语言。通过Markl、Freytag和Kao在项目A、B和C中开发的方法，在云基础设施上解析、优化、并行化、执行和重新优化这样的查询。在这个项目中，开发了特定于IE的操作符，它将文本压缩成结构化表示。此外，我们与项目E合作，开发了一个系统的提取信息的汇总算子，充分考虑了提取信息的不确定性。所有IE操作员都可以配置为采用不同的IE策略，无论是面向高吞吐量，高精度/低不确定性，还是高召回。高级操作员接口必须是独立于域的，而它们的具体实例需要容易地适应手头的文本域。这些要求需要简单IE技术、高级NLP和机器学习的仔细平衡。该项目中开发的所有方法将在生物医学领域的大型和现实的IE任务中进行评估。

The main objective of this project is to enable query-based analysis of large quantities of unstructured text. We envision users to formulate IE tasks with the Stratosphere query language. Such a query is parsed, optimized, parallelized, executed, and re-optimized on a Cloud infrastructure by methods developed in projects A, B, and C by Markl, Freytag, and Kao. The IE-specific operators, which crunch text into structured representations, are developed in this project. Furthermore, we develop, in cooperation with the Project E, operators for a systematic aggregation of extracted information that fully take the uncertainty of extracted information into account. All IE operators will be configurable to embrace different IE strategies, either geared towards high throughput, high precision / low uncertainty, or high recall. The high-level operator interfaces must be domain independent, while their concrete instantiations need to be easily adaptable to the text-domain at hand. These requirements call for a carefully balanced mixture of simple IE techniques, advanced NLP, and Machine Learning. All methods developed within this project will be evaluated on large and realistic IE tasks in the biomedical domain.