Applying distributional semantics to trace conceptual change

Here is the abstract of a talk I gave at the AIUCD conference in Rome in January 2017.

screen-shot-2017-01-26-at-10-29-14What 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 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”