Name
Abstract | ||
|---|---|---|
Topic models have been widely used to identify topics in text corpora. It is also known that purely unsupervised models often result in topics that are not comprehensible in applications. In recent years, a number of knowledge-based models have been proposed, which allow the user to input prior knowledge of the domain to produce more coherent and meaningful topics. In this paper, we go one step further to study how the prior knowledge from other domains can be exploited to help topic modeling in the new domain. This problem setting is important from both the application and the learning perspectives because knowledge is inherently accumulative. We human beings gain knowledge gradually and use the old knowledge to help solve new problems. To achieve this objective, existing models have some major difficulties. In this paper, we propose a novel knowledge-based model, called MDK-LDA, which is capable of using prior knowledge from multiple domains. Our evaluation results will demonstrate its effectiveness. |
Year | Venue | Keywords |
|---|---|---|
2013 | IJCAI | multi-domain prior knowledge,new domain,meaningful topic,topic model,knowledge-based model,novel knowledge-based model,input prior knowledge,new problem,old knowledge,multiple domain,prior knowledge,human beings gain knowledge |
Field | DocType | Citations |
Data science,Procedural knowledge,Body of knowledge,Domain knowledge,Computer science,Knowledge-based systems,Text corpus,Multi domain,Artificial intelligence,Knowledge extraction,Topic model,Machine learning | Conference | 30 |
PageRank | References | Authors |
0.81 | 28 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiyuan Chen | 1 | 260 | 18.35 |
| Arjun Mukherjee | 2 | 1050 | 48.65 |
| Bing Liu | 3 | 14486 | 811.80 |
| Meichun Hsu | 4 | 3437 | 778.34 |
| Malú Castellanos | 5 | 857 | 54.71 |
| Riddhiman Ghosh | 6 | 348 | 16.06 |