# Extracting Impact Model Narratives from Social Services' Text

arXiv: https://arxiv.org/abs/2204.09557 · HTML: https://mlsanity.com/paper/2204.09557/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 14 of 14)

- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision](https://mlsanity.com/paper/2006.15509/) (2020)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media](https://mlsanity.com/paper/2104.08116/) (2021)
- [Dependency-Guided LSTM-CRF for Named Entity Recognition](https://mlsanity.com/paper/1909.10148/) (2019)
- [Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization](https://mlsanity.com/paper/2001.08053/) (2020)
- [Towards Lingua Franca Named Entity Recognition with BERT](https://mlsanity.com/paper/1912.01389/) (2019)
- [On Type-Aware Entity Retrieval](https://mlsanity.com/paper/1708.08291/) (2017)
- [Fine-Grained Named Entity Recognition using ELMo and Wikidata](https://mlsanity.com/paper/1904.10503/) (2019)
- [Cross-lingual alignments of ELMo contextual embeddings](https://mlsanity.com/paper/2106.15986/) (2021)
- [Temporal Embeddings and Transformer Models for Narrative Text Understanding](https://mlsanity.com/paper/2003.08811/) (2020)
- [From Textual Information Sources to Linked Data in the Agatha Project](https://mlsanity.com/paper/1909.05359/) (2019)

## Abstract (arXiv)

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.