Name
Abstract | ||
|---|---|---|
Latent Dirichlet allocation defines hidden topics to capture latent semantics in text documents. However, it assumes that all the documents are represented by the same topics, resulting in the "forced topic" problem. To solve this problem, we developed a group latent Dirichlet allocation (GLDA). GLDA uses two kinds of topics: local topics and global topics. The highly related local topics are organized into groups to describe the local semantics, whereas the global topics are shared by all the documents to describe the background semantics. GLDA uses variational inference algorithms for both offline and online data. We evaluated the proposed model for topic modeling and document clustering. Our experimental results indicated that GLDA can achieve a competitive performance when compared with state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s10791-014-9244-9 | Information Retrieval |
Keywords | Field | DocType |
Topic modeling,Latent Dirichlet allocation,Group,Variational inference,Online learning,Document clustering | Dynamic topic model,Online learning,Data mining,Latent Dirichlet allocation,Information retrieval,Document clustering,Computer science,Inference,Topic model,Semantics | Journal |
Volume | Issue | ISSN |
18 | 1 | 1386-4564 |
Citations | PageRank | References |
4 | 0.46 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ximing Li | 1 | 44 | 13.97 |
| Jihong OuYang | 2 | 94 | 15.66 |
| You Lu | 3 | 19 | 5.52 |
| Xiaotang Zhou | 4 | 19 | 4.08 |
| Tian Tian | 5 | 4 | 0.46 |