Author: Beatrice Alex

Capturing neighbourhood characteristics by clustering geoparsed local news

Our paper on “Perceptions of Edinburgh: Capturing neighbourhood characteristics by clustering geoparsed local news” is published here. This work is part of the AIM-CISC project on AI and multimorbidity, where our aim was to capture information about neighbourhoods and communities in local news. 

We collected Edinburgh Evening News accessed through University of Edinburgh Library for a timespan of 5 years and clustered them by neighbourhood into relevant topics.

We also visualised topics across time.

We used the Edinburgh Geoparser to georeference the news articles and determine which the most relevant neighbourhood for each article is. We also used the neighbourhoods of the Scottish Index of Multiple Deprivation to link the news to.

As part of this work, we ran a public involvement and engagement event with members of the public who read local newspapers and have knowledge of the City of Edinburgh.

This work was made possible by combining different datasets, resources and cross-disciplinary expertise and is used for follow-on research identifying which characteristics of neighbourhoods increase vulnerability or promote resilience to ill health (Abubakar, E., et al., under review.).

Earlier this year, our team won the Centre for Data, Culture and Society award for the Best Novel Use of a Digital Method.

“This interdisciplinary project itself makes a strong contribution to understanding community health in Scotland, and the success of the methodology suggests a wider application of this framework to assessing community health via local news in other areas. I especially appreciate the researchers’ testing of their methods to confirm that the correlation they had uncovered was, indeed, reliable!”

The project was funded by NIHR’s AIM funding and is supported by the Advanced Care Research Centre.

Reference: Andreas Grivas, Claire Grover, Richard Tobin, Clare Llewellyn, Eleojo Oluwaseun Abubakar, Chunyu Zheng, Chris Dibben, Alan Marshall, Jamie Pearce, Beatrice Alex,Perceptions of Edinburgh: Capturing neighbourhood characteristics by clustering geoparsed local news, Information Processing & Management, Volume 62, Issue 1, 2025, https://doi.org/10.1016/j.ipm.2024.103910.

Published originally on Medium on the 12th of May 2023

Our collaboration of Gaelic language experts and computational linguists at the University of Edinburgh resulted in an Artificial Intelligence (AI) model uncovering human cheating that happened decades earlier.

We, Prof Will Lamb, in Celtic and Scottish Studies, and Dr Beatrice Alex, Senior Lecturer in text mining at Literatures, Languages and Cultures, are leading an interdisciplinary project creating Scottish Gaelic handwriting recognition (HWR) to convert manuscripts to electronic text. Our team has trained the first Scottish Gaelic Transkribus model for recognising handwriting in Gaelic automatically. Transkribus provides a platform for AI-powered text recognition, transcription and searching of historical documents for different languages and time periods. It allows researchers to train models effectively, even for low resource languages that have fairly little accessible data to begin with.

To train the Scottish Gaelic HWR model, our team used manuscripts from the School of Scottish Studies Archives (SSSA). The SSSA mostly comprises sound recordings relating to cultural life, folklore and traditional arts in Scotland — including songs, tales and verse collected over the last 70 years. Some of these recordings are accompanied by handwritten transcriptions and it’s this data that was used to train the model.

We ran a series of experiments, which were published in the 4th Celtic Language Technology Workshop proceedings [1], and the model is made available for research on the Transkribus website. For the primary hand, the best model achieves a character level accuracy of 98.3% and a word level accuracy of 95.1%.

During the error analysis, we spotted some unusual examples that caused the model to stumble. It struggled to recognise the writing of one hand, in particular. Looking further into the data, we realised that the hand was that of one particular individual who was already known to the Gaelic language experts involved in the project. The handwriting of this person was unusually spaced out, much more so than in typical adult handwriting.

One of the reasons for the poor performance on this hand is that the model doesn’t have sufficient training data for this handwriting style. However, the large gaps between the words (also called inter-word spacing) present a particular challenge to handwriting recognition models and lead to incorrect line splitting, as can be seen in the image above. By treating each word individually, the model is unable to take context into account when recognising words and characters, leading to poor HRW performance.

It turns out that the fieldworker concerned was paid by the page. Presumably, this particular individual decided to make the job more lucrative by spacing out their handwriting and generating more pages of transcription [2]. Little did they know that their work practices would be thrown up by an AI model many years later. Prof Lamb has recently discovered that the fieldworker concerned was called out in the 1950s, which led to a change in payment to a flat rate.

So AI technology cannot only be used by students to generate essays to cheat in assignments as is now feared by many university and college staff with the release of ChatGPT; in this case, it helped to identify an outlier of unusual behaviour. Our team found it amusing that the AI model wasn’t able to recognise this particular handwriting accurately, resurfacing a long forgotten incident of someone trying to make a bit of extra cash in a slightly underhand way.

Beatrice Alex, Will Lamb and Michael Bauer

References

[1] Sinclair, Mark, William Lamb, and Beatrice Alex (2022). Handwriting Recognition for Scottish Gaelic. In Proceedings of the 4th Celtic Language Technology Workshop at LREC 2022 (CLTW 4), Marseille, June 2022, pp.60–70.

[2] Lamb, William (2012) ‘The storyteller, the scribe and a missing man: Hidden influences from printed sources in the Gaelic tales of Duncan and Neil MacDonald’, Oral Tradition, 27/1: 109–160.

New Release

We have released version 1.3 of the Edinburgh Geoparser and updated the accompanying lesson on the Programming Historian. The Geoparser now runs with a free OpenStreetmap visualisation by default. Anita Hawes, Publishing Assistant at Programming Historian, recently made us aware that users of the Geoparser who followed our lesson were asked to enter credit card details when creating the key for using Mapbox for the map visualisation.  We want our language technology to be open and free, so we reacted quickly to fix that.

We have now changed the Geoparser’s visualisation component to use OpenStreetMap tiles by default. OpenStreetMap tiles can be used for light use free of charge (and without signing up to anything) in accordance with their Tile Usage Policy.

If you have a Mapbox account you can continue to use it with the Geoparser by setting the GEOPARSER_MAP_KEY environment variable as before, but make sure you are aware of the possibility that they may charge you if you have given them a credit card number and exceed their limits on free use.

This is the only change we made in v1.3 compared to v1.2.  If you don’t use the visualisation component there is no need to update.

Watch out for Exonyms

An exonym is a place name for which foreigners have a different name, like Munich for München. The main disadvantage of using OpenStreetMap tiles – from the point of view of an English-language geoparser – is that it generally displays maps in the language of the area or country, rather than English. This is a problem for exonyms as a place name on the map might not coincide with the name in the text. Despite this mismatch, it’s actually compelling to see how place names vary in different languages. For example, check out the place name for Hungary:

To help track your locations, the Geoparser visualisation centres the map on the corresponding pin when clicking on a place name that was recognised and is highlighted in the text and it also displays the recognised place name when hovering on the pin (see Figure 1).

Happy New Geoparsing!

Here is an urgent message from Prof Melissa Terras on how to help preserve Ukrainian Cultural Heritage … please spread the word.

Dear Colleagues,

Trusted friends of mine have set up SUCHO, Save Ukrainian Cultural Heritage Online (SUCHO) https://www.sucho.org

They are asking for volunteers to help identify and archive sites and content, while they are still online. You do not have to read Ukrainian or Russian to help. 

You can submit items to be saved: https://docs.google.com/forms/d/e/1FAIpQLSffa64-l6qXqEumAcf38OEOrTFeYZEmF531PNv9ZgzNFbcgxQ/viewform

And Volunteer to help put things in the internet archive, or use more advanced archiving software: https://docs.google.com/forms/d/e/1FAIpQLSc6KbhtEOI8zKsQmKT_waE1XlYEF1E6t-HzJ7Gc1EBfMvMg_A/viewform

Please do share with colleagues, and your students, and your networks. It’s one concrete thing we can do to help Ukraine, from afar.

You may also be interested in following the work of the Ukrainian Library Association, who are coordinating a National Digital Library in Ukraine: https://www.facebook.com/ula.org.ua/

Best wishes, and I hope you are doing ok at this difficult time.
Melissa 
————
Professor Melissa Terras
University of Edinburgh, College of Arts, Humanities and Social Sciences
@melissaterras

Blue skies over Edinburgh, Scotland today. #StandingWithUkraine pic.twitter.com/9BjOLORYPr

— Dr Beatrice Alex (@bea_alex) March 1, 2022

Ten Things We Learned about Fusion Teaching — Reflections on the first EFI Fusion Course