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Automatic Section Recognition in Obituaries
Sabbatino, Valentino; Bostan, Laura Ana Maria; Klinger, Roman (2020): Automatic Section Recognition in Obituaries, in: Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, u. a. (Hrsg.), Proceedings of the Twelfth Language Resources and Evaluation Conference, European Language Resources Association, S. 817–825.
Faculty/Chair:
Title of the compilation:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Editors:
Calzolari, Nicoletta
Béchet, Frédéric
Blache, Philippe
Choukri, Khalid
Cieri, Christopher
Declerck, Thierry
Goggi, Sara
Isahara, Hitoshi
Maegaard, Bente
Mariani, Joseph
Mazo, Hélène
Moreno, Asuncion
Odijk, Jan
Piperidis, Stelios
Conference:
Twelfth Language Resources and Evaluation Conference ; Marseille, France
Publisher Information:
Year of publication:
2020
Pages:
Language:
English
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.
GND Keywords: ; ; ;
Computerlinguistik
Nachruf
Korpus <Linguistik>
Statistisches Modell
Keywords:
Obituaries
DDC Classification:
RVK Classification:
Type:
Conferenceobject
Activation date:
March 14, 2024
Versioning
Question on publication
Permalink
https://fis.uni-bamberg.de/handle/uniba/93914