Name
Abstract | ||
|---|---|---|
The extraction of spatial semantics is important in many real-world applications such as geographical information systems, robotics and navigation, semantic search, etc. Moreover, spatial semantics are the most relevant semantics related to the visualization of language. The goal of multimodal spatial role labeling task is to extract spatial information from free text while exploiting accompanying images. This task is a multimodal extension of spatial role labeling task which has been previously introduced as a semantic evaluation task in the SemEval series. The multimodal aspect of the task makes it appropriate for the CLEF lab series. In this paper, we provide an overview of the task of multimodal spatial role labeling. We describe the task, sub-tasks, corpora, annotations, evaluation metrics, and the results of the baseline and the task participant. |
Year | Venue | Field |
|---|---|---|
2017 | CLEF | Spatial analysis,Information system,SemEval,Semantic search,Information retrieval,Computer science,Visualization,Natural language processing,Artificial intelligence,Semantics,Clef,Robotics |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Parisa Kordjamshidi | 1 | 143 | 18.52 |
| Taher Rahgooy | 2 | 3 | 4.47 |
| Marie-Francine Moens | 3 | 1750 | 139.27 |
| James Pustejovsky | 4 | 2523 | 334.15 |
| Umar Manzoor | 5 | 14 | 5.03 |
| Kirk Roberts | 6 | 334 | 39.86 |