Name
Abstract | ||
|---|---|---|
We consider the challenging problem of entity typing over an extremely fine grained set of types, wherein a single mention or entity can have many simultaneous and often hierarchically-structured types. Despite the importance of the problem, there is a relative lack of resources in the form of fine-grained, deep type hierarchies aligned to existing knowledge bases. In response, we introduce TypeNet, a dataset of entity types consisting of over 1941 types organized in a hierarchy, obtained by manually annotating a mapping from 1081 Freebase types to WordNet. We also experiment with several models comparable to state-of-the-art systems and explore techniques to incorporate a structure loss on the hierarchy with the standard mention typing loss, as a first step towards future research on this dataset. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computation and Language | Computer science,Typing,Natural language processing,Artificial intelligence,WordNet,Hierarchy |
DocType | Volume | Citations |
Journal | abs/1711.05795 | 1 |
PageRank | References | Authors |
0.36 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shikhar Murty | 1 | 9 | 5.18 |
| Patrick Verga | 2 | 97 | 9.11 |
| Luke Vilnis | 3 | 328 | 17.06 |
| Andrew Kachites McCallumzy | 4 | 19203 | 1588.22 |