  In the last decades, philosophers have begun using empirical data for
conceptual analysis, but corpus-based conceptual analysis has so far failed to
develop, in part because of the absence of reliable methods to automatically
detect concepts in textual data. Previous attempts have shown that topic models
can constitute efficient concept detection heuristics, but while they leverage
the syntagmatic relations in a corpus, they fail to exploit paradigmatic
relations, and thus probably fail to model concepts accurately. In this
article, we show that using a topic model that models concepts on a space of
word embeddings (Hu and Tsujii, 2016) can lead to significant increases in
concept detection performance, as well as enable the target concept to be
expressed in more flexible ways using word vectors.
