# Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning

arXiv: https://arxiv.org/abs/2308.02556 · HTML: https://mlsanity.com/paper/2308.02556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 1 of 1)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)

## Abstract (arXiv)

We present a text mining system to support the exploration of large volumes of text detailing the findings of government inquiries. Despite their historical significance and potential societal impact, key findings of inquiries are often hidden within lengthy documents and remain inaccessible to the general public. We transform the findings of the Irish government's inquiry into industrial schools and through the use of word embedding, text classification and visualisation, present an interactive web-based platform that enables the exploration of the text to uncover new historical insights.