Name
Abstract | ||
|---|---|---|
Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA-based methods in terms of topic definition, topic-document relationship, and learning method. It has been shown to discover significantly more coherent topics and better topic hierarchies. However, HLTA relies on the Expectation-Maximization (EM) algorithm for parameter estimation and hence is not efficient enough to deal with large datasets. In this paper, we propose a method to drastically speed up HLTA using a technique inspired by the advances in the method of moments. Empirical experiments show that our method greatly improves the efficiency of HLTA. It is as efficient as the state-of-the-art LDA-based method for hierarchical topic detection and finds substantially better topics and topic hierarchies. |
Year | Venue | Field |
|---|---|---|
2016 | THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | Computer science,Artificial intelligence,Estimation theory,Hierarchy,Machine learning,Speedup,Method of moments (statistics) |
DocType | Volume | Citations |
Conference | abs/1508.00973 | 10 |
PageRank | References | Authors |
0.57 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peixian Chen | 1 | 19 | 1.57 |
| Nevin .L Zhang | 2 | 895 | 97.21 |
| Leonard K. M. Poon | 3 | 94 | 10.96 |
| Zhourong Chen | 4 | 228 | 12.22 |