Name
Abstract | ||
|---|---|---|
Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. Non-local and non-sequential context is, however, a valuable source of information to improve predictions. In this paper, we introduce GraphIE, a framework that operates over a graph representing a broad set of dependencies between textual units (i.e. words or sentences). The algorithm propagates information between connected nodes through graph convolutions, generating a richer representation that can be exploited to improve word-level predictions. Evaluation on three different tasks -- namely social media, textual and visual information extraction -- shows that GraphIE consistently outperforms the state-of-the-art sequence tagging model by a significant margin. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computation and Language | Graph,Social media,Computer science,Convolution,Information extraction,Natural language processing,Artificial intelligence |
DocType | Volume | Citations |
Journal | abs/1810.13083 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yujie Qian | 1 | 3 | 2.40 |
| Enrico Santus | 2 | 0 | 0.68 |
| Zhijing Jin | 3 | 5 | 2.77 |
| Jiang Guo | 4 | 0 | 0.34 |
| Regina Barzilay | 5 | 3869 | 254.27 |