We introduce an advanced information extraction pipeline to automatically
process very large collections of unstructured textual data for the purpose of
investigative journalism. The pipeline serves as a new input processor for the
upcoming major release of our New/s/leak 2.0 software, which we develop in
cooperation with a large German news organization. The use case is that
journalists receive a large collection of files up to several Gigabytes
containing unknown contents. Collections may originate either from official
disclosures of documents, e.g. Freedom of Information Act requests, or
unofficial data leaks. Our software prepares a visually-aided exploration of
the collection to quickly learn about potential stories contained in the data.
It is based on the automatic extraction of entities and their co-occurrence in
documents. In contrast to comparable projects, we focus on the following three
major requirements particularly serving the use case of investigative
journalism in cross-border collaborations: 1) composition of multiple
state-of-the-art NLP tools for entity extraction, 2) support of multi-lingual
document sets up to 40 languages, 3) fast and easy-to-use extraction of
full-text, metadata and entities from various file formats.

本文介绍了一种信息提取工具，可以自动处理大规模无结构文本数据，支持多语言文档的全文、元数据和实体的提取，重点支持三个主要因素以服务于跨国界新闻调查，最终实现了数据集的快速探索以及潜在故事的挖掘。