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, this http URL 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.

Introduction: An obituary, typically found in newspapers, informs about the recent death of a person, and usually includes a brief biography of the deceased person, which sometimes recounts detailed life stories and anecdotes. Structural elements, styles, formats, and information presented vary slightly from culture to culture or from community to community BIBREF0. Obituaries can be considered to be short essays and contain information on the living family members and information about the upcoming funeral, such as visitation, burial service, and memorial information as well as the cause of death BIBREF0. Similarly to biographies, obituaries represent an interesting type of text because the information contained is usually focused on the values and the qualities of a given human being that is part of a particular community BIBREF1, BIBREF2, BIBREF3. From the digital humanities perspective investigating obituaries also provides an understanding of how the community who writes the obituaries decides what is relevant about life and death. Potential applications that are enabled by having access to large collections of obituaries are finding such themes that are relevant while discussing life and death, investigation of different aspects of social memory BIBREF4, BIBREF5 (finding what is being remembered or chosen to be excluded from an obiturary), investigation of correlations between work or other different themes and the cause of death, analysis of linguistic, structural or cultural differences BIBREF6, and the investigation of different biases and values within a community BIBREF7, BIBREF8, BIBREF9, BIBREF10. More recently, obituaries have been published on dedicated social networks where the mourners who write the obituaries express their emotions and tell stories of the deceased in comments to the obituaries (e. g. Legacy.com, Remembering.CA). These networks facilitate interactions between readers and the family of the deceased BIBREF11. With this paper, we focus on online publications of obituaries which are available online and are in English. Research that builds on top of such data is presumably mostly concerned with a part of the information contained in obituaries. For example, when investigating mortality records BIBREF12, one might only be interested in the Personal Information section. Therefore, we propose to perform zoning as a preprocessing step and publish a corpus and trained models for the sections Personal information (including names of the deceased, birth date, date of death, and cause of death), Biographical sketch, Tribute, Family, and Funeral Information (such as time, place, and date of the funeral). No such resource is currently available to the research community. Our main contributions are therefore (1) to annotate a collection of obituaries, (2) to analyze the corpus and to formulate the task of automatic recognition of structures, (3) to evaluate which models perform best on this task, and (4) to compare the models' results qualitatively and quantitatively. To achieve our goals and as additional support for future research, we publish information how to obtain the data and the annotated dataset as well as the models at http://www.ims.uni-stuttgart.de/data/obituaries.

Question: by how much did their model outperform the other models?
Answer: In terms of macro F1 score their model has 0.65 compared to 0.58 of best other model.