Learning Concept Hierarchies from Text Corpora using Formal Concept
  Analysis
  We present a novel approach to the automatic acquisition of taxonomies or
concept hierarchies from a text corpus. The approach is based on Formal Concept
Analysis (FCA), a method mainly used for the analysis of data, i.e. for
investigating and processing explicitly given information. We follow Harris
distributional hypothesis and model the context of a certain term as a vector
representing syntactic dependencies which are automatically acquired from the
text corpus with a linguistic parser. On the basis of this context information,
FCA produces a lattice that we convert into a special kind of partial order
constituting a concept hierarchy. The approach is evaluated by comparing the
resulting concept hierarchies with hand-crafted taxonomies for two domains:
tourism and finance. We also directly compare our approach with hierarchical
agglomerative clustering as well as with Bi-Section-KMeans as an instance of a
divisive clustering algorithm. Furthermore, we investigate the impact of using
different measures weighting the contribution of each attribute as well as of
applying a particular smoothing technique to cope with data sparseness.
