Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis

TL;DR

This paper introduces a novel method using Formal Concept Analysis to automatically generate concept hierarchies from text corpora, leveraging syntactic dependency vectors and evaluating against handcrafted taxonomies.

Contribution

It presents a new FCA-based approach for taxonomy induction from text, incorporating syntactic dependencies and comparing with clustering methods.

Findings

FCA effectively produces meaningful concept hierarchies.

The approach outperforms hierarchical clustering methods.

Different weighting and smoothing techniques impact hierarchy quality.

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.