Automatic Section Recognition in Obituaries

Valentino Sabbatino, Laura Ana Maria Bostan, Roman Klinger


Abstract
Obituaries contain information about people’s values across times and cultures, which makes them a useful resource for exploring cultural history. They are typically structured similarly, with sections corresponding to Personal Information, Biographical Sketch, Characteristics, Family, Gratitude, Tribute, Funeral Information and Other aspects of the person. To make this information available for further studies, we propose a statistical model which recognizes these sections. To achieve that, we collect a corpus of 20058 English obituaries from TheDaily Item, Remembering.CA and The London Free Press. The evaluation of our annotation guidelines with three annotators on 1008 obituaries shows a substantial agreement of Fleiss κ = 0.87. Formulated as an automatic segmentation task, a convolutional neural network outperforms bag-of-words and embedding-based BiLSTMs and BiLSTM-CRFs with a micro F1 = 0.81.
Anthology ID:
2020.lrec-1.102
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
817–825
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.102
DOI:
Bibkey:
Cite (ACL):
Valentino Sabbatino, Laura Ana Maria Bostan, and Roman Klinger. 2020. Automatic Section Recognition in Obituaries. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 817–825, Marseille, France. European Language Resources Association.
Cite (Informal):
Automatic Section Recognition in Obituaries (Sabbatino et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.102.pdf