Abstract
Literature is to some degree a snapshot of the time it was written in and the societal attitudes of the time. Not all depictions are pleasant or in-line with modern-day sensibilities; this becomes problematic when the prevalent depictions over a large body of work are negatively biased, leading to their normalisation. Many much-loved and much-read classics are set in periods of heightened social inequality: slavery, pre-womens' rights movements, colonialism, etc. In this paper, we exploit known text co-occurrence metrics with respect to token-level level contexts to identify prevailing themes associated with known problematic descriptors. We see that prevalent, negative depictions are perpetuated by classic literature. We propose that such a methodology could form the basis of a system for making explicit such problematic associations, for interested parties: such as, sensitivity coordinators of publishing houses, library curators, or organisations concerned with social justice.
| Original language | English |
|---|---|
| Title of host publication | 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING) |
| Publisher | European Language Resources Association (ELRA) |
| Pages | 13734-13739 |
| Number of pages | 6 |
| ISBN (Print) | 978-249381410-4 |
| Publication status | Published - 2024 |
Keywords
- bias in literature
- charged terms
- corpus linguistics