Name
Abstract | ||
|---|---|---|
Obtaining common sense knowledge using current information extraction techniques is extremely challenging. In this work, we instead propose to derive simple common sense statements from fully annotated object detection corpora such as the Microsoft Common Objects in Context dataset. We show that many thousands of common sense facts can be extracted from such corpora at high quality. Furthermore, using WordNet and a novel submodular k-coverage formulation, we are able to generalize our initial set of common sense assertions to unseen objects and uncover over 400k potentially useful facts. |
Year | Venue | Field |
|---|---|---|
2016 | HLT-NAACL | Commonsense knowledge,Object detection,Common sense,Computer science,Submodular set function,Information extraction,Artificial intelligence,Natural language processing,WordNet |
DocType | Citations | PageRank |
Conference | 4 | 0.43 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Yatskar | 1 | 176 | 11.14 |
| Vicente Ordonez | 2 | 1418 | 69.65 |
| Ali Farhadi | 3 | 4492 | 190.40 |