Authorship clustering using multi-headed recurrent neural networks

08/16/2016
by   Douglas Bagnall, et al.
0

A recurrent neural network that has been trained to separately model the language of several documents by unknown authors is used to measure similarity between the documents. It is able to find clues of common authorship even when the documents are very short and about disparate topics. While it is easy to make statistically significant predictions regarding authorship, it is difficult to group documents into definite clusters with high accuracy.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/14/2016

SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents

We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence ...
research
04/13/2005

Learning from Web: Review of Approaches

Knowledge discovery is defined as non-trivial extraction of implicit, pr...
research
03/17/2018

Experiments with Neural Networks for Small and Large Scale Authorship Verification

We propose two models for a special case of authorship verification prob...
research
04/26/2019

Think Again Networks and the Delta Loss

This short paper introduces an abstraction called Think Again Networks (...
research
03/04/2020

Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks

The main source of information regarding ancient Mesopotamian history an...
research
06/19/2023

Grammatical gender in Swedish is predictable using recurrent neural networks

The grammatical gender of Swedish nouns is a mystery. While there are fe...
research
05/01/2022

Molecular Identification from AFM images using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks

Despite being the main tool to visualize molecules at the atomic scale, ...

Please sign up or login with your details

Forgot password? Click here to reset