# Community Learning: Understanding A Community Through NLP for Positive Impact

arXiv: https://arxiv.org/abs/2210.00590 · HTML: https://mlsanity.com/paper/2210.00590/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 2 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Hope Speech Detection: A Computational Analysis of the Voice of Peace](https://mlsanity.com/paper/1909.12940/) (2019)
- [SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks](https://mlsanity.com/paper/1906.02399/) (2019)
- [Empathy and Hope: Resource Transfer to Model Inter-country Social Media Dynamics](https://mlsanity.com/paper/2106.12044/) (2021)
- [Scalable Knowledge Graph Construction from Twitter](https://mlsanity.com/paper/1906.05986/) (2019)
- [Semantic Foundations of Seraph Continuous Graph Query Language](https://mlsanity.com/paper/2111.09228/) (2021)
- [LotRec: A Recommender for Urban Vacant Lot Conversion](https://mlsanity.com/paper/2202.02481/) (2022)

## Abstract (arXiv)

A post-pandemic world resulted in economic upheaval, particularly for the cities' communities. While significant work in NLP4PI focuses on national and international events, there is a gap in bringing such state-of-the-art methods into the community development field. In order to help with community development, we must learn about the communities we develop. To that end, we propose the task of community learning as a computational task of extracting natural language data about the community, transforming and loading it into a suitable knowledge graph structure for further downstream applications. We study two particular cases of homelessness and education in showing the visualization capabilities of a knowledge graph, and also discuss other usefulness such a model can provide.