Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 2256-2264 |
| Number of pages | 9 |
| Journal | Journal of the American Society for Information Science and Technology |
| Volume | 64 |
| Issue number | 11 |
| DOIs | |
| State | Published - Nov 2013 |
Keywords
- machine learning
- natural language processing
- text mining
ASJC Scopus subject areas
- Software
- Information Systems
- Human-Computer Interaction
- Computer Networks and Communications
- Artificial Intelligence
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