2005 LearningConceptHierFromText

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Notes

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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2005 LearningConceptHierFromText	Steffen Staab Philipp Cimiano Andreas Hotho			Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis			http://www.aaai.org/Papers/JAIR/Vol24/JAIR-2409.pdf