One way we can represent text in a way that a machine can interpret is with a word vector. A word vector is simply a numerical representation of a word within a corpus (a body of text, often a series of documents), usually consisting of a series of numbers in a specified sequence. This type of representation is used for a variety of Natural Language Processing tasks – for instance measuring the similarity between two documents.
A new blog post by the team uses a couple of R packages and a method for creating word vectors with a neural net, called GloVe, to produce a series of vectors which give useful clues as to the semantic links between words in a corpus. The method is then used to analyse the printed summaries of the State Papers Online, and show how they can be used to understand how the association between words and concepts changed over the course of the seventeenth century.
Read the full post here
Much of the work on the Networking Archives project has been using the metadata (people, dates, places) of correspondence rather than the content itself. Here we investigate applying text mining techniques to the printed summaries.
Most of the quantitative research on the Networking Archives project has been using the metadata from the digitised correspondence of State Papers Online. Metadata in this sense means everything except the content of the letters: including author names, recipient names, date, place of sending and so on, in the research of seventeenth-century intelligencing. Gale State Papers Online brings together a number of historical primary sources, not only the manuscript images from the State Papers, but also full text versions of the ‘Calendars of State Papers’, a set of printed finding aids mostly produced in the nineteenth century. These printed summaries represent another huge store of data available to us which we also use in the analysis of the data.
As anyone who has worked with the calendars will tell you, they have been produced to very different standards and as such they interpret the documents they represent in very different ways. They tend to suffer from an identity crisis: never quite sure if they should be purely a manuscript finding aid or a more useful description. In addition, as there’s no inherent logic behind the inconsistencies other than changing editorial policies, it’s hard to get a sense of in what way exactly they are inconsistent. Data analysis can help with this, by analysing the entire dataset at scale, to understand the changing shape of the printed calendars by time, topic, and office.
Read the full post here.