Name
Abstract | ||
|---|---|---|
In this paper we propose a domain-independent text segmentation method, which consists of three components. Latent Dirichlet allocation (LDA) is employed to compute words semantic distribution, and we measure semantic similarity by the Fisher kernel. Finally global best segmentation is achieved by dynamic programming. Experiments on Chinese data sets with the technique show it can be effective. Introducing latent semantic information, our algorithm is robust on irregular-sized segments. |
Year | Venue | Keywords |
|---|---|---|
2008 | ACL (Short Papers) | lda-based fisher kernel,semantic similarity,global best segmentation,domain-independent text segmentation method,introducing latent semantic information,fisher kernel,words semantic distribution,irregular-sized segment,chinese data set,latent dirichlet allocation,dynamic programming,text segmentation |
Field | DocType | Volume |
Semantic similarity,Latent Dirichlet allocation,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Explicit semantic analysis,Text segmentation,Probabilistic latent semantic analysis,Artificial intelligence,Fisher kernel,Machine learning | Conference | P08-2 |
Citations | PageRank | References |
21 | 1.16 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Sun | 1 | 21 | 1.16 |
| Runxin Li | 2 | 33 | 2.89 |
| Dingsheng Luo | 3 | 46 | 11.61 |
| Xihong Wu | 4 | 279 | 53.02 |