Here is the abstract of a talk I gave at the AIUCD conference in Rome in January 2017.
What we talk about when we talk about concepts – Applying distributional semantics on Dutch historical newspapers to trace conceptual change
Word embeddings – vector representations of words that embed words in a so-called semantic space where the vectors of semantically similar words lie close together – are increasingly used for semantic searches in large text corpora. Word vector distances can be used to build semantic networks of words. This closely resembles the notion of semantic fields that humanities scholars are familiar with.
We have previously shown how word embeddings, as produced by a popular implementation word2vec, can be used to trace concepts through time without the dependency of particular keywords (Kenter et.al. 2014). However, there are two main challenges that come with the use of word embeddings to represent concepts and conceptual change for the study of history. Firstly: commensurability. The use of computational techniques like word2vec demands choices of practical or technical nature. How do we legitimize these choices in terms of conceptual theory? Secondly: dependency on data. Do the results of word embedding techniques provide insights into real conceptual change, or do they merely reflect arbitrary biases in the underlying data? Doorgaan met het lezen van “Applying distributional semantics to trace conceptual change”
Historical newspapers have traditionally been popular sources to study public mentalities and collective cultures within historical scholarship. At the same time, they have been known as notoriously time-consuming and complex to analyze. The recent digitization of newspapers and the use of computers to gain access to the growing mass of digital corpora of historical news media are altering the historian’s heuristic process in fundamental ways.
I like experimenting with text analysis tools like 




