The Early Modern Dutch Mediascape. Detecting Media Mentions in Chronicles Using Word Embeddings and CRF

A. Lassche, R. Morante
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Abstract

While the production of information in the European early modern period is a well-researched topic, the question how people were engaging with the information explosion that occurred in early modern Europe, is still underexposed. This paper presents the annotations and experiments aimed at exploring whether we can automatically extract media related information (source, perception, and receiver) from a corpus of early modern Dutch chronicles in order to get insight in the mediascape of early modern middle class people from a historic perspective. In a number of classification experiments with Conditional Random Fields, three categories of features are tested: (i) raw and binary word embedding features, (ii) lexicon features, and (iii) character features. Overall, the classifier that uses raw embeddings performs slightly better. However, given that the best F-scores are around 0.60, we conclude that the machine learning approach needs to be combined with a close reading approach for the results to be useful to answer history research questions.
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近代早期荷兰媒体景观。使用词嵌入和CRF检测编年史中的媒体提及
虽然欧洲近代早期的信息生产是一个研究得很好的话题,但人们如何参与近代早期欧洲发生的信息爆炸的问题仍然没有得到充分的关注。本文通过注释和实验,探索是否可以从荷兰早期现代编年史语料库中自动提取媒体相关信息(来源、感知和接受者),从而从历史的角度洞察早期现代中产阶级的媒体景观。在一些条件随机场的分类实验中,测试了三类特征:(i)原始和二进制词嵌入特征,(ii)词汇特征,(iii)字符特征。总的来说,使用原始嵌入的分类器性能稍好一些。然而,考虑到最好的f分数约为0.60,我们得出结论,机器学习方法需要与细读方法相结合,以便结果对回答历史研究问题有用。
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