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Learning concept hierarchies from text corpora using formal concept analysis (2005)

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by Andreas Hotho , Steffen Staab
Venue:J. Artif. Intell. Res
Citations:160 - 5 self
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BibTeX

@ARTICLE{Hotho05learningconcept,
    author = {Andreas Hotho and Steffen Staab},
    title = {Learning concept hierarchies from text corpora using formal concept analysis},
    journal = {J. Artif. Intell. Res},
    year = {2005},
    pages = {24--305}
}

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Abstract

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. 1.

Keyphrases

concept hierarchy    text corpus    formal concept analysis    context information    automatic acquisition    linguistic parser    certain term    special kind    divisive clustering algorithm    syntactic dependency    hand-crafted taxonomy    partial order    novel approach    different measure    harris distributional hypothesis    hierarchical agglomerative clustering    data sparseness    particular smoothing technique   

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