Learning Concept Hierarchies from Text Corpora
using Formal Concept Analysis
Philipp Cimiano, Andreas Hotho and Steffen Staab
Institue AIFB, University of Karlsruhe
Knowledge and Data Engineering Group, University of Kassel
Institute for Computer Science, University of Koblenz-Landau
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.