Practical extraction of disaster-relevant information from social media
2013pp. 1021–1024
Citations Over TimeTop 1% of 2013 papers
Abstract
During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this paper, we study the nature of social-media content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We evaluate our techniques over our two datasets through a set of carefully designed experiments. We also test our methods over a non-disaster dataset to show that our extraction model is useful for extracting information from socially-generated content in general.
Related Papers
- → Bidirectional integrated random fields for human behaviour understanding(2012)10 cited
- → WNUT 2020 Shared Task-1: Conditional Random Field(CRF) based Named Entity Recognition(NER) for Wet Lab Protocols(2020)5 cited
- Named entity recognition in Chinese medical records based on cascaded conditional random field(2014)
- → Human behavior recognition based on fractal conditional random field(2013)2 cited
- Human behavior recognition based on stratified fractal conditional random field(2013)