历史模型和序列源

M. Piotrowski
{"title":"历史模型和序列源","authors":"M. Piotrowski","doi":"10.21825/jeps.v4i1.10226","DOIUrl":null,"url":null,"abstract":"Serial sources such as records, registers, and inventories are the ‘classic’ sources for quantitative history. Unstructured, narrative texts such as newspaper articles or reports were out of reach for historical analyses, both for practical reasons — availability, time needed for manual processing — and for methodological reasons: manual coding of texts is notoriously difficult and hampered by low inter-coder reliability. The recent availability of large amounts of digitized sources allows for the application of natural language processing, which has the potential to overcome these problems. However, the automatic evaluation of large amounts of texts — and historical texts in particular — for historical research also brings new challenges. First of all, it requires a source criticism that goes beyond the individual source and also considers the corpus as a whole. It is a well-known problem in corpus linguistics to determine the ‘balancedness’ of a corpus, but when analyzing the content of texts rather than ‘just’ the language, determining the ‘meaningfulness’ of a corpus is even more important. Second, automatic analyses require operationalizable descriptions of the information you are looking for. Third, automatically produced results require interpretation, in particular, when — as in history — the ultimate research question is qualitative, not quantitative. This, finally, poses the question, whether the insights gained could inform formal, i.e., machine-processable, models, which could serve as foundation and stepping stones for further research.","PeriodicalId":142850,"journal":{"name":"Journal of European Periodical Studies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Historical Models and Serial Sources\",\"authors\":\"M. Piotrowski\",\"doi\":\"10.21825/jeps.v4i1.10226\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Serial sources such as records, registers, and inventories are the ‘classic’ sources for quantitative history. Unstructured, narrative texts such as newspaper articles or reports were out of reach for historical analyses, both for practical reasons — availability, time needed for manual processing — and for methodological reasons: manual coding of texts is notoriously difficult and hampered by low inter-coder reliability. The recent availability of large amounts of digitized sources allows for the application of natural language processing, which has the potential to overcome these problems. However, the automatic evaluation of large amounts of texts — and historical texts in particular — for historical research also brings new challenges. First of all, it requires a source criticism that goes beyond the individual source and also considers the corpus as a whole. It is a well-known problem in corpus linguistics to determine the ‘balancedness’ of a corpus, but when analyzing the content of texts rather than ‘just’ the language, determining the ‘meaningfulness’ of a corpus is even more important. Second, automatic analyses require operationalizable descriptions of the information you are looking for. Third, automatically produced results require interpretation, in particular, when — as in history — the ultimate research question is qualitative, not quantitative. This, finally, poses the question, whether the insights gained could inform formal, i.e., machine-processable, models, which could serve as foundation and stepping stones for further research.\",\"PeriodicalId\":142850,\"journal\":{\"name\":\"Journal of European Periodical Studies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of European Periodical Studies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21825/jeps.v4i1.10226\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of European Periodical Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21825/jeps.v4i1.10226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

诸如记录、寄存器和库存之类的序列源是定量历史的“经典”源。非结构化的、叙述性的文本,如报纸文章或报告,不适合进行历史分析,这既有实际原因——可用性、手工处理所需的时间——也有方法上的原因:文本的手工编码是出了名的困难,而且受到编码器间可靠性低的阻碍。最近大量数字化资源的可用性允许自然语言处理的应用,这有可能克服这些问题。然而,对大量文本尤其是历史文本的自动评价也给历史研究带来了新的挑战。首先,它需要一种超越单个来源的来源批评,并将语料库作为一个整体来考虑。确定语料库的“平衡性”是语料库语言学中一个众所周知的问题,但当分析文本内容而不仅仅是语言时,确定语料库的“意义”就更加重要了。其次,自动分析需要对您正在查找的信息进行可操作的描述。第三,自动产生的结果需要解释,特别是当最终的研究问题是定性的,而不是定量的——就像在历史上一样。最后,这提出了一个问题,即所获得的见解是否可以为正式的,即机器可处理的模型提供信息,这些模型可以作为进一步研究的基础和垫脚石。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Historical Models and Serial Sources
Serial sources such as records, registers, and inventories are the ‘classic’ sources for quantitative history. Unstructured, narrative texts such as newspaper articles or reports were out of reach for historical analyses, both for practical reasons — availability, time needed for manual processing — and for methodological reasons: manual coding of texts is notoriously difficult and hampered by low inter-coder reliability. The recent availability of large amounts of digitized sources allows for the application of natural language processing, which has the potential to overcome these problems. However, the automatic evaluation of large amounts of texts — and historical texts in particular — for historical research also brings new challenges. First of all, it requires a source criticism that goes beyond the individual source and also considers the corpus as a whole. It is a well-known problem in corpus linguistics to determine the ‘balancedness’ of a corpus, but when analyzing the content of texts rather than ‘just’ the language, determining the ‘meaningfulness’ of a corpus is even more important. Second, automatic analyses require operationalizable descriptions of the information you are looking for. Third, automatically produced results require interpretation, in particular, when — as in history — the ultimate research question is qualitative, not quantitative. This, finally, poses the question, whether the insights gained could inform formal, i.e., machine-processable, models, which could serve as foundation and stepping stones for further research.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信