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SUMMARY:Realms of rule: Exploring ‘hidden geographies’ in medieval cor
 pora through spatial humanities - Ian Gregory
DTSTART;TZID=Europe/Amsterdam:20240927T113000
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DTSTAMP:20261012T024632Z
UID:pretalx-spathum24-HWWY8X@pretalx.com
DESCRIPTION:There is a tendency in the spatial humanities to focus more on
  the modern era where sources\, both textual and cartographic\, are compar
 atively rich. Recent studies\, such as the Map of Early Modern London proj
 ect (https://mapoflondon.uvic.ca/)\, and Pelagios project (https://pelagio
 s.org/)\, also show the potential for spatial humanities techniques in the
  Early Modern and Classical periods respectively. However\, as yet\, compa
 ratively few similar studies have focused on the medieval period. Reasons 
 for this might in part be due to the unevenness of textual sources from th
 e Middle Ages\, and also their variety and heterogeneity. This paper aims 
 to address this by exploring the potential of using natural language proce
 ssing (NLP) techniques to explore an English medieval corpus.\nThe paper s
 tarts by describing the creation of a suitable corpus for analysis. This c
 onsists of a collection of digitised state and administrative records in t
 he form of Calendars of Charter and Patent Rolls\, originally printed in t
 he 19th century but now accessible digitally via British History Online. U
 nder the Plantagenet kings of England (12th-15th centuries CE)\, these rec
 ords were the main written instruments through which the royal administrat
 ion 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 
 from the Joy Welch Fund\, the authors have been engaged with using the Cal
 endars as a basis for an exploratory study to ‘excavate’ hidden geogra
 phies of the Plantagenet realm\, and in particular to determine (1) what p
 laces were of interest to English government in the 13th and 14th centurie
 s\, and (2) what do contemporaries have to say about these places in the C
 alendars?\nTo answer these questions we begin with a multi-pronged approac
 h to simplifying the textual sources for machine processing. As with many 
 NLP pipelines\, we began by downloading and converting the corpus data to 
 use consistent UTF-8 formatting and a simple XML schema in order to repres
 ent the document structure. Inspired by existing approaches applied for mo
 dern texts by the Spatial Narratives project (https://spacetimenarratives.
 github.io/)\, we adapted a pipeline of NLP tools to lemmatise the input (m
 atch terms to their dictionary headwords)\, part-of-speech tag (assign maj
 or word classes such as noun\, verb\, adjective and adverb)\, and semantic
 ally tag the text with semantic fields from the PyMUSAS system (https://py
 pi.org/project/pymusas/) to group words and phrases together into major to
 pic areas. We also cross referenced locations in the text using the Survey
  of English Place Names (https://epns.nottingham.ac.uk) combining the mode
 rn and historic geographic names to create a geographic lemma. \nThe resul
 ting network of text\, associated annotations and alternate forms was stor
 ed in a graph database (Neo4J) for subsequent explorative analysis via col
 location and other relationships. This enables us to take a very source-le
 d approach to understanding the corpora and the geographies that they cont
 ain as well as helping to support more hypothesis-driven approaches. Takin
 g one example\, that of medieval town formation\, searching in the graph-s
 pace 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 
 Figure 1 we present a fragment of the much larger graph showing a subset o
 f the full annotations available within our data as the density of data ev
 en in a relatively small corpus precludes representing the entire graph.\n
  \nFigure 1: A subgraph of a larger corpus-graph demonstrating the associa
 ted tags and their relations.\nUsing these techniques we can extract paths
  through the data connecting individual concepts\, places\, people\, or ot
 her entities along either corpus tokens (the source texts) or conceptual r
 elationships (links between times\, places\, normal forms and others). The
  next stage of work is to define graphlet or fragment templates and evalua
 te their relative accuracy for extracting the information. This paper repo
 rts on these findings and their significance\, as well as the next steps f
 or visualisations of social networks of the Plantagenet realm\, as we seek
  to understand and demonstrate the potential digital tools have for explor
 ing 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 p
 aper that illustrates the potential for spatial humanities approaches in h
 elping to understand medieval geographies.
LOCATION:MG1 00.04 Hörsaal
URL:https://pretalx.com/spathum24/talk/HWWY8X/
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