Unsupervised Discovery of Biographical Structure from Text

We present a method for discovering abstract event classes in biographies, based on a probabilistic latent-variable model. Taking as input timestamped text, we exploit latent correlations among events to learn a set of event classes (such as B , G H S , and B C ), along with the typical times in a p...

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Vydané v:	Transactions of the Association for Computational Linguistics Ročník 2; s. 363 - 376
Hlavní autori:	Bamman, David, Smith, Noah A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.12.2014 MIT Press Journals, The The MIT Press
Predmet:	Ablation Biographies Computer generated language analysis Divorce Encyclopedias Gender High school graduates Information sources Internet Marriage Secondary schools Statistical analysis Variants Websites Women
ISSN:	2307-387X, 2307-387X
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