Beyond Clustering: Sub-DAG Discovery for Categorising Documents

We study the problem of generating DAG-structured category hierarchies over a given set of documents associated with "importance" scores. Example application includes automatically generating Wikipedia disambiguation pages for a set of articles having click counts associated with them. Unlike previous works, which focus on clustering the set of documents using the category hierarchy as features, we directly pose the problem as that of finding a DAG structured generative mode that has maximum likelihood of generating the observed "importance" scores for each document where documents are modeled as the leaf nodes in the DAG structure. Desirable properties of the categories in the inferred DAG-structured hierarchy include document coverage and category relevance, each of which, we show, is naturally modeled by our generative model. We propose two different algorithms for estimating the model parameters. One by modeling the DAG as a Bayesian Network and estimating its parameters via Gibbs Sampling; and the other by estimating the path probabilities using the Expectation Maximization algorithm. We empirically evaluate our method on the problem of automatically generating Wikipedia disambiguation pages using human generated clusterings as the ground truth. We find that our framework improves upon the baselines according to the F1 score and Entropy that are used as standard metrics to evaluate the hierarchical clustering.