Extracting Impact Model Narratives from Social Services' Text

TL;DR

This paper introduces a novel named entity recognition approach tailored for social service texts, enabling extraction of impact narratives and supporting analysis of service sequences and client impacts.

Contribution

It presents the first NER model specifically designed for social service entities, facilitating impact model analysis from unstructured social service descriptions.

Findings

Achieved empirically evaluated NER performance on social service corpus.

Demonstrated extraction of ontological entities like needs and satisfiers.

Enabled hypothesis generation for impact model queries.

Abstract

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.