Exploitation of Diverse Data via Automatic Adaptation of Knowledge Extraction Software

无论是研究文献还是社交媒体，当前一代的语言处理在从大量非结构化文本中提取有用信息方面取得了相当大的成功。然而，适应一个新的领域往往是一个费力的过程，既涉及不同类型的数据(例如，新闻通讯社与专利文献)，也涉及给定领域中使用的术语(例如，在医疗实践与制药研究中)。人类可以在很小的数据集上执行这些任务，但面对大量增加的电子文本，人类面临着挑战。Evoes项目正在利用分布相似性技术来加速定制的关键组件--概念的识别，以及将术语与概念联系起来的术语的创建或改编。

The current generation of language processing has had considerable success in extracting useful information from large amounts of unstructured text, whether this is research literature or social media. However, adapting to a new domain is often a laborious process, with respect both to diverse types of data (e.g. newswire vs. patent literature) and to the terminology used in a given domain (e.g. in medical practice vs. pharmaceutical research). Humans can perform these tasks on small data sets, but face a challenge in the face of massively increasing amounts of electronic text. The EVOKES project is exploiting distributional similarity techniques to accelerate key components of customisation - the recognition of concepts, and the creation or adaptation of terminologies that link terms to concepts.