Folgert Karsdorp

Authenticating the writings of Julius Caesar

We shed new light on the authenticity of the writings of Julius Caesar.Hirtius, one of Caesars generals, must have contributed to Caesars writings.We benchmark two authorship verification systems on publicly available data sets.We test on both modern data sets, and Latin texts from Antiquity.We show how computational methods inform traditional authentication studies. In this paper, we shed new light on the authenticity of the Corpus Caesarianum, a group of five commentaries describing the campaigns of Julius Caesar (100-44 BC), the founder of the Roman empire. While Caesar himself has authored at least part of these commentaries, the authorship of the rest of the texts remains a puzzle that has persisted for nineteen centuries. In particular, the role of Caesars general Aulus Hirtius, who has claimed a role in shaping the corpus, has remained in contention. Determining the authorship of documents is an increasingly important authentication problem in information and computer science, with valuable applications, ranging from the domain of art history to counter-terrorism research. We describe two state-of-the-art authorship verification systems and benchmark them on 6 present-day evaluation corpora, as well as a Latin benchmark dataset. Regarding Caesars writings, our analyses allow us to establish that Hirtiuss claims to part of the corpus must be considered legitimate. We thus demonstrate how computational methods constitute a valuable methodological complement to traditional, expert-based approaches to document authentication.

Mining the Twentieth Century’s History from the Time Magazine Corpus

In this paper we report on an explorative study of the history of the twentieth cen- tury from a lexical point of view. As data, we use a diachronic collection of 270,000+ English-language articles har- vested from the electronic archive of the well-known Time Magazine (1923–2006). We attempt to automatically identify significant shifts in the vocabulary used in this corpus using efficient, yet unsupervised computational methods, such as Parsimonious Language Models. We offer a qualitative interpretation of the outcome of our experiments in the light of momen- tous events in the twentieth century, such as the Second World War or the rise of the Internet. This paper follows up on a recent string of frequentist approaches to studying cultural history (‘Culturomics’), in which the evolution of human culture is studied from a quantitative perspective, on the basis of lexical statistics extracted from large, textual data sets.

MOMFER: A Search Engine of Thompson's Motif-Index of Folk Literature

More than fifty years after the first edition of Thompsons seminal Motif-Indexof Folk Literature, we present an online search engine tailored to fully disclose the index digitally. This search engine, called MOMFER, greatly enhances the searchability of the Motif-Index and provides exciting new ways to explore the collection. This is enabled by our use of modern techniques from both natural language processing and information retrieval. The key feature of the search tool is the way in which it allows users to search the Motif-Index for semantic concepts, such as ‘mythical animals’, ‘mortality’, or ‘emotions’. This paper will explain the motivations for creating the search tool, explicate the production process, and show in a number of case studies how the search tool can be used to explore the index in innovative ways.

The Love Equation: Computational Modeling of Romantic Relationships in French Classical Drama

We report on building a computational model of romantic relationships in a corpus of historical literary texts. We frame this task as a ranking problem in which, for a given character, we try to assign the highest rank to the character with whom (s)he is most likely to be romantically involved. As data we use a publicly available corpus of French 17th and 18th century plays (http://www.theatre-classique.fr/) which is well suited for this type of analysis because of the rich markup it provides (e.g. indications of characters speaking). We focus on distributional, so-called second-order features, which capture how speakers are contextually embedded in the texts. At a mean reciprocal rate (MRR) of 0.9 and MRR@1 of 0.81, our results are encouraging, suggesting that this approach might be successfully extended to other forms of social interactions in literature, such as antagonism or social power relations.

The structure and evolution of story networks.

With this study, we advance the understanding about the processes through which stories are retold. A collection of story retellings can be considered as a network of stories, in which links between stories represent pre-textual (or ancestral) relationships. This study provides a mechanistic understanding of the structure and evolution of such story networks: we construct a story network for a large diachronic collection of Dutch literary retellings of Red Riding Hood, and compare this network to one derived from a corpus of paper chain letters. In the analysis, we first provide empirical evidence that the formation of these story networks is subject to age-dependent selection processes with a strong lopsidedness towards shorter time-spans between stories and their pre-texts (i.e. ‘young’ story versions are preferred in producing new versions). Subsequently, we systematically compare these findings with and among predictions of various formal models of network growth to determine more precisely which kinds of attractiveness are also at play or might even be preferred as explicatory models. By carefully studying the structure and evolution of the two story networks, then, we show that existing stories are differentially preferred to function as a new versions pre-text given three types of attractiveness: (i) frequency-based and (ii) model-based attractiveness which (iii) decays in time.

Animacy detection in stories

This paper presents a linguistically uninformed computational model for animacy classification. The model makes use of word n-grams in combination with lower dimensional word embedding representations that are learned from a web-scale corpus. We compare the model to a number of linguistically informed models that use features such as dependency tags and show competitive results. We apply our animacy classifier to a large collection of Dutch folktales to obtain a list of all characters in the stories. We then draw a semantic map of all automatically extracted characters which provides a unique entrance point to the collection.

De relatie tussen schema’s en analogische verbindingen

In deze bijdrage willen we aandacht besteden aan de rol die analogie speelt in het theoretisch model van Construction Morphology zoals ontwikkeld door Booij (2010). In Construction Morphology worden woordvormingspatronen en productiviteit verantwoord door constructionele schema‘s en subschema‘s. Analogie wordt geinterpreteerd als strikt locale analogie die kan leiden tot een nieuwvorming op basis van een modelwoord. Schema‘s en analogie zijn dus complementair en verschillen in de graad van abstractie, waarbij analogie het uitgangspunt kan zijn voor het ontstaan van een schema. Regelmatige productieve woordvorming en analogische nieuwvorming zijn in deze benadering – anders dan bijvoorbeeld in generatieve benaderingen – geen absolute tegenstellingen, maar ze moeten desondanks volgens Booij wel duidelijk worden onderscheiden van elkaar. Wij willen echter laten zien dat het model wel degelijk ruimte biedt voor een ruimer analogiebegrip, waarbij analogie kan worden gezien als het centrale mechanisme van (ook productieve) woordvorming.