Paradigmatic language change occurs, when paradigmatically related words with similar usage rise or fall together. Such change is the rule rather than an exception. Words rarely increase or decrease in isolation but together with similar words. In the short term, this is usually due to thematic change, but in the longer term, also grammatical preferences change. We present an approach to visually explore paradigmatic change by reducing the dimensionality of and correlating the two main factors involved: Frequency change and distributional semantics of words. Frequency change is reduced to one dimension by means of fitting the logistic growth curves to the observed word frequencies in fixed intervals (e.g. year or decade). Semantics of words reduced to two dimensions such that words with similar usage contexts are positioned closely together. This is accomplished by reducing the very high dimensional representation of word usage context in two steps. Neural network based word embeddings and t-Distributed Stochastic Neighbour Embedding.