Options
Novel2Vec: Characterising 19th Century Fiction via Word Embeddings
Date Issued
2016-09-21
Date Available
2017-02-17T15:28:25Z
Abstract
Recently, considerable attention has been paid to word embedding algorithms inspired by neural network models. Given a large textual corpus, these algorithms attempt to derive a set of vectors which represent the corpus vocabulary in a new embedded space. This representation can provide a useful means of measuring the underlying similarity between words. Here we investigate this property in the context of annotated texts of 19th-century fiction by the authors Jane Austen, Charles Dickens, and Arthur Conan Doyle. We demonstrate that building word embeddings on these texts can provide us with an insight into how characters group differently under different conditions, allowing us to make comparisons across different novels and authors. These results suggest that word embeddings can potentially provide a useful tool in supporting quantitative literary analysis.
Sponsorship
Irish Research Council
Science Foundation Ireland
Type of Material
Conference Publication
Keywords
Web versions
Language
English
Status of Item
Peer reviewed
Conference Details
24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), University College Dublin, Dublin, Ireland, 20-21 September 2016
This item is made available under a Creative Commons License
File(s)
Owning collection
Views
2680
Last Week
1
1
Acquisition Date
Mar 28, 2024
Mar 28, 2024
Downloads
1340
Last Week
3
3
Acquisition Date
Mar 28, 2024
Mar 28, 2024