Extracting Impact Model Narratives from Social Services' Text
This addresses the need for analyzing social service narratives to understand impact models, though it appears incremental as it applies existing NER techniques to a new domain.
The paper tackles the problem of extracting impact model narratives from social services text by proposing a named entity recognition (NER) architecture specifically for social service entities, showing it can sequence services and impacted clients from unstructured text.
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.