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UID:pretalx-spathum24-HWWY8X@pretalx.com
DTSTART;TZID=CET:20240927T113000
DTEND;TZID=CET:20240927T120000
DESCRIPTION:There is a tendency in the spatial humanities to focus more on 
 the modern era where sources\, both textual and cartographic\, are compara
 tively rich. Recent studies\, such as the Map of Early Modern London proje
 ct (https://mapoflondon.uvic.ca/)\, and Pelagios project (https://pelagios
 .org/)\, also show the potential for spatial humanities techniques in the 
 Early Modern and Classical periods respectively. However\, as yet\, compar
 atively few similar studies have focused on the medieval period. Reasons f
 or this might in part be due to the unevenness of textual sources from the
  Middle Ages\, and also their variety and heterogeneity. This paper aims t
 o address this by exploring the potential of using natural language proces
 sing (NLP) techniques to explore an English medieval corpus.\nThe paper st
 arts by describing the creation of a suitable corpus for analysis. This co
 nsists of a collection of digitised state and administrative records in th
 e form of Calendars of Charter and Patent Rolls\, originally printed in th
 e 19th century but now accessible digitally via British History Online. Un
 der the Plantagenet kings of England (12th-15th centuries CE)\, these reco
 rds were the main written instruments through which the royal administrati
 on and its officials ruled the realm. They provide documentation on legal 
 and bureaucratic decisions concerning a wide range of governance issues\, 
 including information on property\, people and places. Thanks to a grant f
 rom the Joy Welch Fund\, the authors have been engaged with using the Cale
 ndars as a basis for an exploratory study to ‘excavate’ hidden geograp
 hies of the Plantagenet realm\, and in particular to determine (1) what pl
 aces were of interest to English government in the 13th and 14th centuries
 \, and (2) what do contemporaries have to say about these places in the Ca
 lendars?\nTo answer these questions we begin with a multi-pronged approach
  to simplifying the textual sources for machine processing. As with many N
 LP pipelines\, we began by downloading and converting the corpus data to u
 se consistent UTF-8 formatting and a simple XML schema in order to represe
 nt the document structure. Inspired by existing approaches applied for mod
 ern texts by the Spatial Narratives project (https://spacetimenarratives.g
 ithub.io/)\, we adapted a pipeline of NLP tools to lemmatise the input (ma
 tch terms to their dictionary headwords)\, part-of-speech tag (assign majo
 r word classes such as noun\, verb\, adjective and adverb)\, and semantica
 lly tag the text with semantic fields from the PyMUSAS system (https://pyp
 i.org/project/pymusas/) to group words and phrases together into major top
 ic areas. We also cross referenced locations in the text using the Survey 
 of English Place Names (https://epns.nottingham.ac.uk) combining the moder
 n and historic geographic names to create a geographic lemma. \nThe result
 ing network of text\, associated annotations and alternate forms was store
 d in a graph database (Neo4J) for subsequent explorative analysis via coll
 ocation and other relationships. This enables us to take a very source-led
  approach to understanding the corpora and the geographies that they conta
 in as well as helping to support more hypothesis-driven approaches. Taking
  one example\, that of medieval town formation\, searching in the graph-sp
 ace starting from a list of manually selected seed terms relevant to town 
 formation enabled us to refer to the locations in any of their forms. In F
 igure 1 we present a fragment of the much larger graph showing a subset of
  the full annotations available within our data as the density of data eve
 n in a relatively small corpus precludes representing the entire graph.\n 
 \nFigure 1: A subgraph of a larger corpus-graph demonstrating the associat
 ed tags and their relations.\nUsing these techniques we can extract paths 
 through the data connecting individual concepts\, places\, people\, or oth
 er entities along either corpus tokens (the source texts) or conceptual re
 lationships (links between times\, places\, normal forms and others). The 
 next stage of work is to define graphlet or fragment templates and evaluat
 e their relative accuracy for extracting the information. This paper repor
 ts on these findings and their significance\, as well as the next steps fo
 r visualisations of social networks of the Plantagenet realm\, as we seek 
 to understand and demonstrate the potential digital tools have for explori
 ng how geographically dispersed people and places across medieval Britain 
 and Ireland were governed through spatial connections that linked them to 
 the centralised rule of the monarch. In so doing we will also present a pa
 per that illustrates the potential for spatial humanities approaches in he
 lping to understand medieval geographies.
DTSTAMP:20260306T112520Z
LOCATION:MG1 00.04 Hörsaal
SUMMARY:Realms of rule: Exploring ‘hidden geographies’ in medieval corp
 ora through spatial humanities - Ian Gregory
URL:https://pretalx.com/spathum24/talk/HWWY8X/
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