“Vaderland”, “Volk” and “Natie”: Semantic Change Related to Nationalism in D utch Literature Between 1700 and 1880 Captured with Dynamic B ernoulli Word Embeddings (2022)

Abstract: Languages can respond to external events in various ways - the creation of new words or named entities, additional senses might develop for already existing words or the valence of words can change. In this work, we explore the semantic shift of the Dutch words “natie” (“nation”), “volk” (“people”) and “vaderland” (“fatherland”) over a period that is known for the rise of nationalism in Europe: 1700-1880. The semantic change is measured by means of Dynamic Bernoulli Word Embeddings which allow for comparison between word embeddings over different time slices. The word embeddings were generated based on Dutch fiction literature divided over different decades. From the analysis of the absolute drifts, it appears that the word “natie” underwent a relatively small drift. However, the drifts of “vaderland’” and “volk”’ show multiple peaks, culminating around the turn of the nineteenth century. To verify whether this semantic change can indeed be attributed to nationalistic movements, a detailed analysis of the nearest neighbours of the target words is provided. From the analysis, it appears that “natie”, “volk” and “vaderlan”’ became more nationalistically-loaded over time.

## introduction the nineteenth century is often characterized as the era of modernity and nationalism @xcite @xcite . however, the development of the modernist mindset did not happen overnight. @xcite call this cultural transition period from the early modern period to the modern period the sattelzeit or saddle period. in this period, from roughly 1750 to 1850, the reading public expanded, people became used to thinking about the past and the future, ideologies such as nationalism arose, and abstract concepts became more politically applicable. this paper aims to contribute to the study of the development of the cultural thought of nationalism during the sattelzeit in dutch society by researching fiction literature from 1700 to 1880. by employing a dynamic word embedding model we examine whether the (literary) contexts of three target words "natie" ("nation"), "volk" ("people") and "vaderland" ("fatherland") have changed over the course of the eighteenth century and nineteenth century. the dynamic word embedding model allows us to measure to what extent the contexts might have changed by quantifying the semantic drift leveraging the target words' embeddings. by doing so we aim to establish whether there is indeed a measurable semantic change or drift that coincides with the upcoming cultural and political thoughts of the era. ## related work in history, studying how concepts have changed over time is called begriffsgeschichte or conceptual history. influential in conceptual history are the works from @xcite , @xcite and @xcite . van sas (1999) studied different representations of the dutch nation by looking at words expressing concepts related to nationalism over the centuries, using political texts and literature from the fifteenth century to 1940. in the field of digital humanities, dynamic word embeddings can be employed to measure how words change over time. word embeddings are distributional representations of words constructed based on their distribution in texts, i.e. these embeddings quantify how often words co-occur with other words in (preferably large) corpora. this idea is based on the distributional hypothesis, which presumes that the meaning of a word can be derived from its linguistic context @xcite . semantic representations can be learned using natural language processing techniques, such as word2vec @xcite , that automat-ically learn associations by leveraging information from large corpora. these distributional methods have been proven suitable to capture (broad) semantic changes in large generic corpora @xcite @xcite . more recently, word embeddings have also been used to investigate semantic shifts in historical contexts, e.g. shifts in gender bias in historical newspapers @xcite , changes in gender and ethnic stereotypes @xcite , evolution of concepts @xcite , study of parliamentary debates (van lange and futselaar, 2018) and others. a practical difficulty with dynamic word embeddings arises when attempting to compare the embeddings over different time periods. this problem is referred to as the alignment problem and different solutions have been proposed @xcite . a second challenge, especially when dealing with historical data, is the fact that large corpora are required to train word embeddings (e.g. the model of @xcite required a dataset of 100,000,000 words per time slice). @xcite , @xcite , @xcite and @xcite proposed a dynamic word embedding model for handling such sparse data. for the current research we employ the dynamic bernoulli embedding model of @xcite . @xcite demonstrated that dynamic bernoulli embeddings give good predictive performance for time windows with sparse data. moreover, their method is able to capture changes of rare words. both dynamic filtering of skip-gram and dynamic bernoulli embedding are able to detect drifts within very sparse datasets. however, the dynamic bernoulli embedding has been shown to keep words that do not change over time more stable @xcite . in our experiments, we apply the dynamic bernoulli embedding model to dutch literature to study the semantic shift of words related to nationalism, with the goal to contribute to the analysis of the historical discourse on nationalism. ## results the dynamic bernoulli embedding models are evaluated with the bernoulli positive likelihood on the validation set, or l pos . this metric is used to select the hyperparameter settings. the results of the experiments are represented in in target word embedding mean absolute drift 0.0253 "natie" ("nation") 0.0644 "volk" ("folk") 0.1800 "vaderland" ("fatherland") 0.1128 figure 1 illustrates the drift of the target words over the different time slices. this graph shows that while "natie" has a small absolute drift, the drift becomes a bit larger over time, but it stays below the average drift of words. the word "vaderland"shows three peaks in their drift over time. the first peak coincides roughly with the emergence of the word "vaderland" in dutch book titles @xcite . the second peak is around 1780, which is the decade of the politicization of the dutch enlightenment @xcite . the third peak of drift happens in the decade of the dutch constitutional reform of 1848 and the revolutions in europe of the same year. aside from the absolute drift and the drift over time, the nearest neighbours of the target words in the embedding of a specific time slice were analyzed. the nearest neighbors can be understood as the word most often used in a similar context of the target words, and are thus considered semantically close according to the distributional hypothesis. due to the page-limit, we restrict ourselves to a brief illustration of the nearest neighbors of the word "vaderland". we allude at some of the findings we observed for the word "volk" and "natie". the word "volk" changes fast from the last quarter of the eighteenth century onwards. the nearest neighbors of "volk" in the earlier decades of the eighteenth century are mainly related to biblical themes. de @xcite explains that biblical literature was popular in the eighteenth century. the interpretation of the word "volk" changes from "people of israel" towards the meaning of "people as a mob" in later decades, which explains the larger drift from the 1780s onwards. for "vaderland", the nearest neighbors are "geboorteland" ("country of birth") and "geboortegrond" ("place of birth"). among the neighbors are also some more affectionate words such as "dierbare" ("dear") and "dierbaarst" ("dearest") and "vrijgevochten" ("free-spirited"). aside from that, among the top 10 nearest neighbours, we can find terms alluding at a fatherland's past: "wapenroem" ("fame of arms"), "onafhankelijkheid" ("independence"), and, specific to the dutch past, "bataven" ("batavian(s)"). from 1780 onwards, the words "nederland" ("the netherlands") and "vlaander-land" ("flanders") are present in the top 10. we give an example of the top 10 nearest neighbours of the word "vaderland" in for "volk", the word "oproerig" ("rebellious") is the nearest neighbor for every decade except the last one. other words like "oproer" ("rebellion"), "muitziek" and "muitzucht" ("mutinious") emphasize the dangerous/negative connotation of "people" ("people as a mob"). these words furthermore appear more frequently and get a higher position in the top 10 nearest neighbors in the later decades. "natie" showed almost no variation over time, as was expected by the low absolute drift. for "natie", many of its nearest neighbours across the different time slices were words referring to institutions while "natie" didn't undergo a traceable semantic shift according to the final mode, we nevertheless looked into the nearest neighbours over the decades. "handeldrijvende" ("trading") is the nearest neighbor in all decades, followed by "naäpen" ("copying", as in what a copycat does) and "nationaliteit" ("nationality"). further down the neighbouring words we encounter institutions (universities ("universiteiten"), courts ("gerechtshoven", "rechtbanken"), governments ("gouvernments"), people's government ("volksregering"), and republic ("republiek"). ## conclusions and future work in this study the emergence and development of nationalism in dutch culture is studied by looking at the semantic change of the target words "natie", "volk" and "vaderland". this is done by applying the dynamic bernoulli model, proposed by @xcite , to dutch fiction literature between 1700-1880, during the emergence and development of nation building and nationalism @xcite . furthermore, through the analysis of the nearest neighbours we show how the contextual meaning of the target words changed. to the best of our knowledge, this is the first research that uses dynamic bernoulli embeddings to contribute to an analysis of historical discourse, which in this case is the debate on the origins and spread of nationalism in the netherlands. the results of this study show that there are measurable changes in the dynamic word embeddings of words related to nationalism over the course of the eighteenth and nineteenth century during a period that is known as the sattelzeit.