  We propose a novel graph-based approach for constructing concept hierarchy
from a large text corpus. Our algorithm, GraBTax, incorporates both statistical
co-occurrences and lexical similarity in optimizing the structure of the
taxonomy. To automatically generate topic-dependent taxonomies from a large
text corpus, GraBTax first extracts topical terms and their relationships from
the corpus. The algorithm then constructs a weighted graph representing topics
and their associations. A graph partitioning algorithm is then used to
recursively partition the topic graph into a taxonomy. For evaluation, we apply
GraBTax to articles, primarily computer science, in the CiteSeerX digital
library and search engine. The quality of the resulting concept hierarchy is
assessed by both human judges and comparison with Wikipedia categories.
