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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| Vydáno v: | Transactions of the Association for Computational Linguistics Ročník 2; s. 363 - 376 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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One Rogers Street, Cambridge, MA 02142-1209, USA
MIT Press
01.12.2014
MIT Press Journals, The The MIT Press |
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| ISSN: | 2307-387X, 2307-387X |
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| Abstract | 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
person’s life when those events occur. In a quantitative evaluation at the task
of predicting a person’s age for a given event, we find that our generative
model outperforms a strong linear regression baseline, along with simpler
variants of the model that ablate some features. The abstract event classes that
we learn allow us to perform a large-scale analysis of 242,970 Wikipedia
biographies. Though it is known that women are greatly underrepresented on
Wikipedia—not only as editors (Wikipedia, 2011) but also as subjects of articles
(Reagle and Rhue, 2011)—we find that there is a bias in their
as well, with biographies of women
containing significantly more emphasis on events of marriage and divorce than
biographies of men. |
|---|---|
| AbstractList | 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 Born, Graduates High School, and Becomes Citizen), along with the typical times in a person’s life when those events occur. In a quantitative evaluation at the task of predicting a person’s age for a given event, we find that our generative model outperforms a strong linear regression baseline, along with simpler variants of the model that ablate some features. The abstract event classes that we learn allow us to perform a large-scale analysis of 242,970 Wikipedia biographies. Though it is known that women are greatly underrepresented on Wikipedia—not only as editors (Wikipedia, 2011) but also as subjects of articles (Reagle and Rhue, 2011)—we find that there is a bias in their characterization as well, with biographies of women containing significantly more emphasis on events of marriage and divorce than biographies of men. 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 person’s life when those events occur. In a quantitative evaluation at the task of predicting a person’s age for a given event, we find that our generative model outperforms a strong linear regression baseline, along with simpler variants of the model that ablate some features. The abstract event classes that we learn allow us to perform a large-scale analysis of 242,970 Wikipedia biographies. Though it is known that women are greatly underrepresented on Wikipedia—not only as editors (Wikipedia, 2011) but also as subjects of articles (Reagle and Rhue, 2011)—we find that there is a bias in their as well, with biographies of women containing significantly more emphasis on events of marriage and divorce than biographies of men. |
| Author | Bamman, David Smith, Noah A. |
| Author_xml | – sequence: 1 givenname: David surname: Bamman fullname: Bamman, David email: dbamman@cs.cmu.edu organization: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA, dbamman@cs.cmu.edu – sequence: 2 givenname: Noah A. surname: Smith fullname: Smith, Noah A. email: nasmith@cs.cmu.edu organization: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA, nasmith@cs.cmu.edu |
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| Cites_doi | 10.1214/aos/1056562461 10.1146/annurev.soc.26.1.667 10.1017/S1351324900000048 10.2307/2061812 10.1177/036319908000500206 10.1111/j.1083-6101.2006.00316.x 10.1353/dem.0.0122 10.1093/llc/fqp035 10.1146/annurev.so.12.080186.000545 10.1002/asi.21577 10.1080/00332747.1991.11024555 10.2307/2061690 10.1177/036319907600100103 10.1073/pnas.0307752101 |
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| References | Conway Mike (p_19) 2010; 25 Blei David M. (p_6) 2007; 1 Lillard Lee A. (p_37) 1995; 32 Reinhold Steffen (p_55) 2010; 47 Callahan Ewa S. (p_9) 2011; 62 Blei David M. (p_7) 2003; 3 Kolbitsch Josef (p_35) 2006; 12 Justeson John S (p_34) 1995; 1 Shanahan Michael J. (p_59) 2000; 26 Neal Radford M (p_49) 2003 Modell John (p_47) 1980; 5 Hogan Dennis P. (p_32) 1986; 12 Pfeil Ulrike (p_52) 2006; 12 Griffiths Thomas L (p_28) 2004; 101 Elder Glen (p_23) 1991; 54 Lillard Lee A. (p_36) 1993; 30 Modell John (p_46) 1976; 1 |
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on a probabilistic latent-variable model. Taking as input timestamped text, we... We present a method for discovering abstract event classes in biographies, based on a probabilistic latent-variable model. Taking as input timestamped text, we... |
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| SubjectTerms | Ablation Biographies Computer generated language analysis Divorce Encyclopedias Gender High school graduates Information sources Internet Marriage Secondary schools Statistical analysis Variants Websites Women |
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| Title | Unsupervised Discovery of Biographical Structure from Text |
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