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2021
Journal article
Neural Language Models for Nineteenth-Century English
We present four types of neural language models trained on a large historical dataset of books in English, published between 1760-1900 and comprised of ~5.1 billion tokens. The language model architectures include static (word2vec and fastText) and contextualized models (BERT and Flair). For each architecture, we trained a model instance...Hosseini, Kasra ; Beelen, Kaspar ; Colavizza, Giovanni ; Coll Ardanuy, Mariona
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2019
Conference paper (unpublished)
Defoe: A Spark-Based Toolbox for Analysing Digital Historical Textual Data
This work presents defoe, a new scalable and portable digital eScience toolbox that enables historical research. It allows for running text mining queries across large datasets, such as historical newspapers and books in parallel via Apache Spark. It handles queries against collections that comprise several XML schemas and physical representations.... -
2020
Conference paper (unpublished)
Assessing the Impact of OCR Quality on Downstream NLP Tasks
A growing volume of heritage data is being digitized and made available as text via optical character recognition (OCR). Scholars and libraries are increasingly using OCR-generated text for retrieval and analysis. However, the process of creating text through OCR introduces varying degrees of error to the text. The impact of...