Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis

By Philipp Cimiano, Andreas Hotho, Steffen Staab. Journal of Artificial Intelligence Research, 2005

645 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/cb81fd17c7522b595af228e50a57dd618ea9b188

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Citations in the corpus

Extracting implicit knowledge from text (2009, methodology): “Calling the generalization problem a case of engineering in the face of sparse data, Clark and Weir (2002) looked at a number of previous methods, one conclusion being that the approach of Li and Abe appears to over-generalize. Cao et al. (2008) gave a distributional method for deriving semantic res”
Ontology Learning from Text: An Overview (2005, background): “In this line, people have mainly exploited hierarchical clustering algorithms to automatically derive term hierarchies from text, e.g. [7], [13], [17].”
SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation (2014, methodology): “Such models are used for the automatic generation of dictionaries, thesauri, ontologies and language correction tools (Cimiano et al., 2005; Biemann, 2005; Li et al., 2006).”