The Semantics of Structure in Large Historical Corpora

Structuring large historical corpora that are too big to be processed manually can take two approaches. The first is an inductive method extracting implicit entities and meaning from textual (and sometimes visual) content. With the help of AI or manually compiled (existing) lists of entities, the entities are converted into information. The second, that Colavizza (2019) calls referential information systems, takes existing reference systems (like archival indexes) and uses them to contextualize individual documents. Both methods are used to turn corpora into computer accessible information systems. Ideally a more complete information system would result from combining both approaches, but in practice they are hard to bridge because of a number of different problems. This paper presents an approach that addresses those problems and combines inductive methods of automated text analysis and information extraction techniques with knowledge of the referential information systems to add rich semantic layers of information to large historical corpora.Making large historical corpora accessible for research usually involves a pipeline of processing steps, ranging from text recognition to entity and event spotting, disambiguation, identification and ideally contextualization (Meroño-Peñuela et al. 2015). In many projects much effort is spent on producing a close-to-perfect text by transcribing, or by a mixed procedure of automatic transcription by Optical Character Recognition (OCR) or Handwritten Text Recognition (HTR) and manual correction of the results, as many of the later elements in the pipeline require high-quality text to work well. There are ways to partially solve OCR or HTR (Handwritten Text Recognition) errors automatically through post-correction (see e.g. Reynaert 2014, Reynaert 2016), or to use word embeddings to overcome matching problems, (e.g. Egense 2017). The most important limitation of this approach is that full-text alone is not enough to make a corpus available for research that is not primarily directed at the text but rather at its information (Hoekstra and Koolen 2018, Upward 2018). Extracting and contextualizing information has many issues such as OCR and HTR errors that make it difficult to use standard Natural Language Processing (NLP) tools like Named Entity Recognition (NER), topic modelling, Part Of Speech (POS) tagging and sentiment analysis, which has been common knowledge for a long time (Lopresti 2008, Traub et al. 2015, Mutuvi et al. 2018, Hill & Hengchen 2019, van Strien et al. 2020). However, solutions for such issues are scarce and badly documented, as argued by, amongst others, Piersma and Ribbens (2013), van Eijnatten et al. (2013) and Leemans et al. (2017).Many archives and libraries have experimented with giving access to their collections by means of their digitized inventories and some have gone a step further, using existing indexes of serial collections (Jeurgens 2016, Colavizza 2019, Head 2003). But these archival referential systems are too coarse for access beyond the document level. However, the existing scholarly apparatus consists of many more reference systems and tools that can be put to good use. Centuries of dealing with these complications have led to a number of convenient and often-employed structures that are part of the printed culture but are often ignored in the translation to digital access (Upward 2018, Opitz 2018).Exploiting Referential Information Systems and Repetitive PhrasesInstead of trying to find latent semantic structures through full-text analysis, these explicit structures allow for finding intended semantic information that is likely not available in another form. Remarkably, many digitization programmes take no advantage of these structures and sometimes do not even digitize them, extracting only the main textual body as plain text.A concrete and relatively simple example, the Resolutions of the Dutch States General is a collection of all resolutions (decisions) of the Dutch Republic from 1576 until 1796 and contains around 440,000 pages. Roughly half of the pages, up to 1703, are handwritten. From 1703 onwards, the States General printed yearly editions for easier access to previous resolutions. The States General met six days a week and kept a list of who was present on what date, followed by a summary of each resolution.