论文标题
从社会服务文本中提取影响模型的叙述
Extracting Impact Model Narratives from Social Services' Text
论文作者
论文摘要
命名实体识别（NER）是叙事提取中的重要任务。作为故事体系，叙事提供了有关故事中事件和人物如何随着时间而发展的见解。本文提出了关于社会目的组织的语料库的NER架构。这是专门针对社会服务实体的NER任务。我们展示了如何将这种方法用于服务的测序，并通过从非结构化文本中提取的信息对客户进行了影响。该方法概述了提取需求和满足感和产生假设的实体的本体论表示的步骤，以回答有关社会目的组织定义的影响模型的疑问。我们对具有经验计算得分的社会服务描述语料库进行评估。
Named entity recognition (NER) is an important task in narration extraction. Narration, as a system of stories, provides insights into how events and characters in the stories develop over time. This paper proposes an architecture for NER on a corpus about social purpose organizations. This is the first NER task specifically targeted at social service entities. We show how this approach can be used for the sequencing of services and impacted clients with information extracted from unstructured text. The methodology outlines steps for extracting ontological representation of entities such as needs and satisfiers and generating hypotheses to answer queries about impact models defined by social purpose organizations. We evaluate the model on a corpus of social service descriptions with empirically calculated score.
