Over the past few decades, the amount of scientific articles and technical
literature has increased exponentially in size. Consequently, there is a great
need for systems that can ingest these documents at scale and make the
contained knowledge discoverable. Unfortunately, both the format of these
documents (e.g. the PDF format or bitmap images) as well as the presentation of
the data (e.g. complex tables) make the extraction of qualitative and
quantitive data extremely challenging. In this paper, we present a modular,
cloud-based platform to ingest documents at scale. This platform, called the
Corpus Conversion Service (CCS), implements a pipeline which allows users to
parse and annotate documents (i.e. collect ground-truth), train
machine-learning classification algorithms and ultimately convert any type of
PDF or bitmap-documents to a structured content representation format. We will
show that each of the modules is scalable due to an asynchronous microservice
architecture and can therefore handle massive amounts of documents.
Furthermore, we will show that our capability to gather ground-truth is
accelerated by machine-learning algorithms by at least one order of magnitude.
This allows us to both gather large amounts of ground-truth in very little time
and obtain very good precision/recall metrics in the range of 99\% with regard
to content conversion to structured output. The CCS platform is currently
deployed on IBM internal infrastructure and serving more than 250 active users
for knowledge-engineering project engagements.

本研究论文介绍了一种名为 Corpus Conversion Service 的模块化云平台，用于海量文档的处理、文本挖掘和机器学习算法的训练，最终将任何类型的 PDF 或位图文档转换为结构化内容表示格式，且能够达到 99％的准确度，并已在 IBM 内部基础设施上部署，并为超过 250 个活跃用户提供服务。