ML service designed to identify the authorship of texts among ten prominent Russian 19th century writers
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Updated
Apr 9, 2024 - Jupyter Notebook
ML service designed to identify the authorship of texts among ten prominent Russian 19th century writers
4 Assignments for the Text and Multimedia Mining course of AI master course at Radboud University
Authorship Attribution for Romanian
An Authorship Attribution Dataset for Detecting Human-Trafficking Operations on Escort Advertisements. (EMNLP 2023)
Solving the Author Problem of “Dream of the Red Chamber” with the Writing Style Indicator以文風指標分析《紅樓夢》的作者爭議問題
Collected solutions from Google Code Jam programming competition (2008-2020).
A Python library that allows easy extraction of a variety of text units within texts...
A package for generating CRediT (Contributor Role Taxonomy) statements
📚📊 BayesianBookworm: A text analysis tool utilizing Bayesian inference to attribute authorship of literary works, initially focusing on the distinctive styles of Austen and Dickens.
A collection of python scripts / jupyter notebooks to construct various datasets across NLP and CV topics.
A repository containing the source code, datasets, and ranked features for the Nested Bigrams method proposed in a paper published in ICDMW. This method is designed for authorship attribution in source code to address cybersecurity issues.
The Python Graphical Authorship Attribution Program — An experimental Python port of the Duquesne University Evaluating Variations in Language Lab's JGAAP.
The Java Graphical Authorship Attribution Program
C# implementation of evllabs's JGAAP
Ati is a web-based application for predicting which famous classic Bulgarian novelist wrote a piece of text (short or long).
✍️ An intelligent system that takes a document and classifies different writing styles within the document using stylometric techniques.
Is your boss a Scott or a Schrute? Find out here!
Authorship Attribution in Social Media & Chat Biometrics & Behavioral Biometrics
SUTD 02.136DH Lyric Poetry Digital Humanities Project (Nominated for Best DH Project Award)
Comparison of classification power (literary authorship attribution case) of word-based, lemma-based, POS-based and mBERT-based document embeddings, as well as their combinations.
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