Automatic Section Recognition in Obituaries

• URL: http://arxiv.org/abs/2002.12699v1
• Date: Fri, 28 Feb 2020 13:20:09 GMT
• Title: Automatic Section Recognition in Obituaries
• Authors: Valentino Sabbatino and Laura Bostan and Roman Klinger
• Abstract summary: We propose a statistical model which recognizes sections of obituaries. We collect a corpus of 20058 English obituaries from TheDaily Item, Remembering.CA and The London Free Press. Formulated as an automatic segmentation task, a convolutional neural network outperforms bag-of-words.
• Score: 10.536415845097661
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 k = 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.

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