Named-Entity Recognition for Disaster Related Filipino News Articles

News articles do not only provide reports but also provide a capable focal point in recognizing people's behavior, decision-making, and information sources before, during, and after wide-scope events, such as natural disasters. The Philippines, which is a country prone to natural disasters has an immense amount of data regarding natural disasters making it hard to process all data manually just to identify the useful and important information. Because of that, the industry needs to find best ways to extract the relevant information. In this paper, the proponents proposed to use named-entity recognition that finds and classifies expressions of special meaning in texts written in a natural language that often holds a key information in a text. The proponents aim to design a model for named-entity recognition using deep learning in disaster related Filipino news articles.