Authorship Attribution
49 papers with code • 0 benchmarks • 0 datasets
Authorship attribution, also known as authorship identification, aims to attribute a previously unseen text of unknown authorship to one of a set of known authors.
Benchmarks
These leaderboards are used to track progress in Authorship Attribution
Most implemented papers
Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution
Convolutional neural networks (CNNs) have demonstrated superior capability for extracting information from raw signals in computer vision.
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
amritabh/conda-gen-text-detection
•
•
Findings (EMNLP) 2021
Recent progress in generative language models has enabled machines to generate astonishingly realistic texts.
Higher Criticism for Discriminating Word-Frequency Tables and Testing Authorship
We apply this measure to authorship attribution challenges, where the goal is to identify the author of a document using other documents whose authorship is known.
Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation
In this work, we use a multilingual knowledge distillation approach to train BERT models to produce sentence embeddings for Ancient Greek text.
Multiple Authors Detection: A Quantitative Analysis of Dream of the Red Chamber
Inspired by the authorship controversy of Dream of the Red Chamber and the application of machine learning in the study of literary stylometry, we develop a rigorous new method for the mathematical analysis of authorship by testing for a so-called chrono-divide in writing styles.
Authorship Attribution Using a Neural Network Language Model
In practice, training language models for individual authors is often expensive because of limited data resources.
Domain Specific Author Attribution Based on Feedforward Neural Network Language Models
In this paper, we present a novel setup of a Neural Network Language Model (NNLM) and apply it to a database of text samples from different authors.
Authorship Attribution Using Text Distortion
A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre.
Authorship Attribution Using Text Distortion
A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre.
An Automated Text Categorization Framework based on Hyperparameter Optimization
The compared datasets include several problems like topic and polarity classification, spam detection, user profiling and authorship attribution.
