Name
Abstract | ||
|---|---|---|
We propose a latent-embedding-structured lifelong learning topic model, called the LLT model, to discover coherent topics from a corpus. Specifically, we exploit latent word embeddings to structure our model and mine word correlation knowledge to assist in topic modeling. During each learning iteration, our model learns new word embeddings based on the topics generated in the previous learning iteration. Experimental results demonstrate that our LLT model is able to generate more coherent topics than state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICSC.2017.15 | 2017 IEEE 11th International Conference on Semantic Computing (ICSC) |
Keywords | Field | DocType |
Lifelong learning,Topic modeling,Latent embeddings | Computer science,Exploit,Artificial intelligence,Natural language processing,Topic model,Lifelong learning,Machine learning | Conference |
ISSN | ISBN | Citations |
2325-6516 | 978-1-5090-4285-2 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingyang Xu | 1 | 2 | 1.37 |
| Ruixin Yang | 2 | 201 | 31.97 |
| Steve Harenberg | 3 | 17 | 5.11 |
| Nagiza F. Samatova | 4 | 861 | 74.04 |