Name
Abstract | ||
|---|---|---|
This paper proposes three modules based on latent topics of documents for alleviating "semantic drift" in bootstrapping entity set expansion. These new modules are added to a discriminative bootstrapping algorithm to realize topic feature generation, negative example selection and entity candidate pruning. In this study, we model latent topics with LDA (Latent Dirichlet Allocation) in an unsupervised way. Experiments show that the accuracy of the extracted entities is improved by 6.7 to 28.2% depending on the domain. |
Year | Venue | Keywords |
|---|---|---|
2011 | ACL (Short Papers) | bootstrapping entity set expansion,topic information,topic feature generation,model latent topic,latent topic,latent dirichlet allocation,new module,semantic drift,entity candidate pruning,discriminative bootstrapping algorithm,negative example selection |
Field | DocType | Volume |
Latent Dirichlet allocation,Computer science,Bootstrapping,Artificial intelligence,Set expansion,Natural language processing,Probabilistic latent semantic analysis,Feature generation,Discriminative model,Machine learning,Semantic change | Conference | P11-2 |
Citations | PageRank | References |
11 | 0.58 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kugatsu Sadamitsu | 1 | 42 | 7.40 |
| Kuniko Saito | 2 | 75 | 7.12 |
| Kenji Imamura | 3 | 42 | 5.60 |
| Gen-ichiro Kikui | 4 | 305 | 33.43 |