A Hierarchical Location Normalization System for Text
This addresses location normalization for text analysis in Chinese administrative contexts, though it appears incremental as it builds on existing co-occurrence constraint frameworks with specific embeddings and ROI expansion.
The paper tackles the problem of extracting hierarchical administrative area information from incomplete location references in text, proposing ROIBase which achieves better performance against feasible solutions for Chinese location normalization.
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.