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Computational methods in the analysis of word meaning: lessons learnt from interdisciplinary projects

Venue: Birkbeck Main Building

Over time new words enter the language, others become obsolete, and existing words acquire new meanings. In Old English thing meant ‘a public assembly’ and now means more generally ‘entity’; chill originally meant ‘to cool’ and has metaphorically been extended to ‘to relax’. The recent digitization efforts have now made it possible to access and mine large digital collections of historical texts using automatic methods, and investigate the question of semantic change at an unprecedented scale.

In this talk I will present my research on developing computational models for semantic change in historical texts, aiming to teach computers to identify such change automatically. I  will share my experience from several interdisciplinary projects in Digital Humanities and Computational Linguistics at The Alan Turing Institute.


Barbara McGillivray is a research fellow at the University of Cambridge and at The Alan Turing Institute, where she runs the Data Science and Digital Humanities special interest group. She holds a degree in Mathematics and one in Classics from the University of Firenze (Italy), and a PhD in Computational Linguistics from the University of Pisa (2010). Before joining the Turing Institute and the University of Cambridge, she worked as a language technologist in the Dictionary division of Oxford University Press and as a data scientist in the Open Research Group of Springer Nature.

Barbara McGillivray’s research lies at the intersection between computational linguistics and historical linguistics and, more broadly, between Data Science and Digital Humanities. Her current research focusses on computational models of semantic change in historical texts. Her first book, Methods in Latin Computational Linguistics, was published by Brill in 2013 ( and her second book, Quantitative Historical Linguistics. A corpus framework, co-authored with Gard B Jenset, was published by Oxford University Press in 2017 (

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