During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this work, we study the nature of social-media content generated during two different natural disasters. We also train a set of models based on conditional random fields to extract valuable information from such content. We evaluate our extraction models over our two disaster-related datasets. We also evaluate our models over one non-disaster dataset to show that our extraction models are useful for extracting information from socially-generated content in general.