  It's natural these days for people to know the local events from massive
documents. Many texts contain location information, such as city name or road
name, which is always incomplete or latent. It's significant to extract the
administrative area of the text and organize the hierarchy of area, called
location normalization. Existing detecting location systems either exclude
hierarchical normalization or present only a few specific regions. We propose a
system named ROIBase that normalizes the text by the Chinese hierarchical
administrative divisions. ROIBase adopts a co-occurrence constraint as the
basic framework to score the hit of the administrative area, achieves the
inference by special embeddings, and expands the recall by the ROI (region of
interest). It has high efficiency and interpretability because it mainly
establishes on the definite knowledge and has less complex logic than the
supervised models. We demonstrate that ROIBase achieves better performance
against feasible solutions and is useful as a strong support system for
location normalization.
