Traditionally, there has been a disconnect between custom-built applications used to solve real-world information extraction problems in industry, and automated learning-based approaches developed in academia. Despite approaches such as transfer-based learning, adapting these to more customised solutions where the task and data may be different, and where training data may be largely unavailable, is still hugely problematic, with the result that many systems still need to be custom-built using expert hand-crafted knowledge, and do not scale. In the legal domain, a traditional slow adopter of technology, black box machine learning-based systems are too untrustworthy to be widely used. In industrial settings, the fine-grained highly specialised knowledge of human experts is still critical, and it is not obvious how to integrate this into automated classification systems. In this paper, we examine two case studies from recent work combining this expert human knowledge with automated NLP technologies.
Natural language processing Ontologies Information extraction
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