Author Attribution
2 papers with code • 0 benchmarks • 1 datasets
Authorship attribution is the task of determining the author of a text.
Benchmarks
These leaderboards are used to track progress in Author Attribution
Taiga Corpus
Latest papers with no code
On the Limitations of Large Language Models (LLMs): False Attribution
We then randomly sampled 162 chunks for human evaluation from each of the annotated books, based on the error margin of 7% and a confidence level of 95% for the book with the most chunks (Great Expectations by Charles Dickens, having 922 chunks).
T5 meets Tybalt: Author Attribution in Early Modern English Drama Using Large Language Models
Large language models have shown breakthrough potential in many NLP domains.
The Word2vec Graph Model for Author Attribution and Genre Detection in Literary Analysis
By using these Word2vec graph based features, we perform classification to perform author attribution and genre detection tasks.
Moving Towards Open Set Incremental Learning: Readily Discovering New Authors
This paper also develops a new metric that measures multiple attributes of clustering open set data.
Authorship Attribution By Consensus Among Multiple Features
Most existing research on authorship attribution uses various lexical, syntactic and semantic features.
Automated Attribution and Intertextual Analysis
In this work, we employ quantitative methods from the realm of statistics and machine learning to develop novel methodologies for author attribution and textual analysis.
Authorship detection of SMS messages using unigrams
We argue that, considering the methods of author attribution, the best method that could be applied to SMS messages is an n-gram method.
