Name
Abstract | ||
|---|---|---|
Recently, supervised topic modeling approaches have received considerable attention. However, the representative labeled latent Dirichlet allocation (L-LDA) method has a tendency to over-focus on the pre-assigned labels, and does not give potentially lost labels and common semantics sufficient consideration. To overcome these problems, we propose an extension of L-LDA, namely supervised labeled latent Dirichlet allocation (SL-LDA), for document categorization. Our model makes two fundamental assumptions, i.e., Prior 1 and Prior 2, that relax the restriction of label sampling and extend the concept of topics. In this paper, we develop a Gibbs expectation-maximization algorithm to learn the SL-LDA model. Quantitative experimental results demonstrate that SL-LDA is competitive with state-of-the-art approaches on both single-label and multi-label corpora. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s10489-014-0595-0 | Applied Intelligence |
Keywords | Field | DocType |
Supervised,Topic modeling,Latent Dirichlet allocation,Multi-label classification | Dynamic topic model,Categorization,Latent Dirichlet allocation,Pattern recognition,Computer science,Pachinko allocation,Multi-label classification,Artificial intelligence,Sampling (statistics),Topic model,Semantics,Machine learning | Journal |
Volume | Issue | ISSN |
42 | 3 | 0924-669X |
Citations | PageRank | References |
1 | 0.36 | 19 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ximing Li | 1 | 44 | 13.97 |
| Jihong OuYang | 2 | 94 | 15.66 |
| Xiaotang Zhou | 3 | 19 | 4.08 |
| You Lu | 4 | 19 | 5.52 |
| Yanhui Liu | 5 | 1 | 0.36 |