Evolution of Semantically Identified Topics

Topics in a corpus evolve in time. Describing the way this evolution occurs helps us to understand the change in the prominence of concepts: one can gain intuition about what concepts become more important in a given topic, which substitute others, or which concepts become related. By defining topics as weighted collections of concepts from a fixed taxonomy, it is possible to know if said evolution occurs within a branch of taxonomy or if hitherto unknown semantic relationships are formed / dissolved with time. In this work, we analyze a corpus of financial news and reveal the evolution of topics composed of concepts from the STW thesaurus. We show that using a thesaurus for building representations of documents is useful. Furthermore, the different abstraction levels encoded in a taxonomy are helpful for detecting different types of topics.