Summary of The Detection and Understanding Of Fictional Discourse, by Andrew Piper et al.
The Detection and Understanding of Fictional Discourse
by Andrew Piper, Haiqi Zhou
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents various classification experiments focused on detecting fictional discourse. It leverages a diverse set of datasets, including professionally published fiction, historical fiction, fanfiction, Reddit stories, folk tales, GPT-generated stories, and anglophone world literature. The authors also introduce a new feature set called “supersenses” that enables semantic generalization. By detecting fictional discourse, this research can contribute to enriching large cultural heritage archives and understanding the unique characteristics of fictional storytelling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to figure out if something is made-up or not. It looks at lots of different kinds of stories, like books, internet posts, and old tales, to see how well it can tell what’s real and what’s just made-up. The researchers also came up with a new way to help the computer understand what words mean, which makes it better at telling stories apart. |
Keywords
* Artificial intelligence * Classification * Discourse * Generalization * Gpt
