Mari Hatavara, Kirsi Sandberg, Mykola Andrushchenko, Sari Hälikkö, J. Nummenmaa, Timo Nummenmaa, Jaakko Peltonen, Matti Hyvärinen
{"title":"Computational recognition of narratives","authors":"Mari Hatavara, Kirsi Sandberg, Mykola Andrushchenko, Sari Hälikkö, J. Nummenmaa, Timo Nummenmaa, Jaakko Peltonen, Matti Hyvärinen","doi":"10.1075/ni.22028.hat","DOIUrl":null,"url":null,"abstract":"\n Computational recognition of narratives, if successful, would find innumerable applications with large digitized\n datasets. Systematic identification of narratives in the text flow could significantly contribute to such pivotal questions as\n where, when, and how narratives are employed. This paper discusses an approach to extract narratives from two datasets, Finnish\n parliamentary records (1980–2021) and oral history interviews with former Finnish MPs (1988–2018). Our study was based on an\n iterative approach, proceeding from original expert readings to a rule-based, computational approach that was elaborated with the\n help of annotated samples and annotation scheme. Annotated samples and computationally found extracts were compared, and a good\n correspondence was found. In this paper, we exhibit and compare the results from annotation and rule-based approach, and discuss\n examples of correctly and incorrectly found narrative sections. We consider that all attempts at recognizing and extracting\n narratives are definition dependent, and feed back to narrative theory.","PeriodicalId":46671,"journal":{"name":"Narrative Inquiry","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Narrative Inquiry","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1075/ni.22028.hat","RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMMUNICATION","Score":null,"Total":0}
引用次数: 0
Abstract
Computational recognition of narratives, if successful, would find innumerable applications with large digitized
datasets. Systematic identification of narratives in the text flow could significantly contribute to such pivotal questions as
where, when, and how narratives are employed. This paper discusses an approach to extract narratives from two datasets, Finnish
parliamentary records (1980–2021) and oral history interviews with former Finnish MPs (1988–2018). Our study was based on an
iterative approach, proceeding from original expert readings to a rule-based, computational approach that was elaborated with the
help of annotated samples and annotation scheme. Annotated samples and computationally found extracts were compared, and a good
correspondence was found. In this paper, we exhibit and compare the results from annotation and rule-based approach, and discuss
examples of correctly and incorrectly found narrative sections. We consider that all attempts at recognizing and extracting
narratives are definition dependent, and feed back to narrative theory.
期刊介绍：
Narrative Inquiry is devoted to providing a forum for theoretical, empirical, and methodological work on narrative. Articles appearing in Narrative Inquiry draw upon a variety of approaches and methodologies in the study of narrative as a way to give contour to experience, tradition, and values to next generations. Particular emphasis is placed on theoretical approaches to narrative and the analysis of narratives in human interaction, including those practiced by researchers in psychology, linguistics and related disciplines.
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