Name
Abstract | ||
|---|---|---|
Topic taxonomies present a multi-level view of a document collection, where general topics live towards the top of the taxonomy and more specific topics live towards the bottom. Topic taxonomies allow users to quickly drill down into their topic of interest to find documents. We show that hierarchies of documents, where documents live at the inner nodes of the hierarchy-tree can also be inferred by combining document text with inter-document links. We present a Bayesian generative model by which an explicit hierarchy of documents is created. Experiments on three document-graph data sets shows that the generated document hierarchies are able to fit the observed data, and that the levels in the constructed document hierarchy represent practical groupings. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2396761.2396843 | CIKM |
Keywords | Field | DocType |
specific topic,observed data,explicit hierarchy,document hierarchy,document collection,topic taxonomy,document graph,document text,document-topic hierarchy,bayesian generative model,document-graph data,general topic,hierarchical clustering,topic models | Hierarchical clustering,Data mining,Graph,Information retrieval,Document clustering,Computer science,Drill down,Topic model,Hierarchy,Bayesian probability,Generative model | Conference |
Citations | PageRank | References |
7 | 0.45 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tim Weninger | 1 | 576 | 46.14 |
| Yonatan Bisk | 2 | 196 | 17.54 |
| Jiawei Han | 3 | 43085 | 3824.48 |