Name
Title | ||
|---|---|---|
Large-scale cross-document coreference using distributed inference and hierarchical models |
Abstract | ||
|---|---|---|
Cross-document coreference, the task of grouping all the mentions of each entity in a document collection, arises in information extraction and automated knowledge base construction. For large collections, it is clearly impractical to consider all possible groupings of mentions into distinct entities. To solve the problem we propose two ideas: (a) a distributed inference technique that uses parallelism to enable large scale processing, and (b) a hierarchical model of coreference that represents uncertainty over multiple granularities of entities to facilitate more effective approximate inference. To evaluate these ideas, we constructed a labeled corpus of 1.5 million disambiguated mentions in Web pages by selecting link anchors referring to Wikipedia entities. We show that the combination of the hierarchical model with distributed inference quickly obtains high accuracy (with error reduction of 38%) on this large dataset, demonstrating the scalability of our approach. |
Year | Venue | Keywords |
|---|---|---|
2011 | ACL | cross-document coreference,large scale processing,effective approximate inference,automated knowledge base construction,wikipedia entity,large collection,large-scale cross-document coreference,hierarchical model,web page,large dataset,inference technique |
Field | DocType | Volume |
Coreference,Web page,Inference,Computer science,Approximate inference,Information extraction,Natural language processing,Artificial intelligence,Knowledge base,Hierarchical database model,Machine learning,Scalability | Conference | P11-1 |
Citations | PageRank | References |
65 | 2.36 | 32 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sameer Singh | 1 | 1060 | 71.63 |
| Amarnag Subramanya | 2 | 422 | 24.53 |
| Fernando Pereira | 3 | 17717 | 2124.79 |
| Andrew Kachites McCallumzy | 4 | 19203 | 1588.22 |