A generic unsupervised method for decomposing multi-author documents
Given an unsegmented multi-author text, we wish to automatically separate out distinct authorial threads. We present a novel, entirely unsupervised, method that achieves strong results on multiple testbeds, including those for which authorial threads are topically identical. Unlike previous work, our method requires no specialized linguistic tools and can be easily applied to any text.
machine learning natural language processing text mining