Neural Language Models for Nineteenth-Century English
Name
48-761-1-PB.pdf
Description
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Size
614.81 KB
Format
Adobe PDF
Checksum (CRC64NVME)
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Resource type
Journal article
Date published
2021
Abstract
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 using the whole dataset. Additionally, we trained separate instances on text published before 1850 for the two static models, and four instances considering different time slices for BERT. Our models have already been used in various downstream tasks where they consistently improved performance. In this paper, we describe how the models have been created and outline their reuse potential.
Contributor (organisation)
British Library
Living with Machines
Project(s)
Living with Machines
Funder
| Funder name | Awards |
Arts and Humanities Research Council | AH/S01179X/1 |
Journal title
Journal of Open Humanities Data
Publisher
Ubiquity Press
Official URL
Collection