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Providing timely accessibility reminders of a point-of-interest (POI) plays avital role in improving user satisfaction of finding places and making visitingdecisions. However, it is difficult to keep the POI database in sync with thereal-world counterparts due to the dynamic nature of business changes. Toalleviate this problem, we formulate and present a practical solution thatjointly extracts POI mentions and identifies their coupled accessibility labelsfrom unstructured text. We approach this task as a sequence tagging problem,where the goal is to produce <POI name, accessibility label> pairs fromunstructured text. This task is challenging because of two main issues: (1) POInames are often newly-coined words so as to successfully register new entitiesor brands and (2) there may exist multiple pairs in the text, whichnecessitates dealing with one-to-many or many-to-one mapping to make each POIcoupled with its accessibility label. To this end, we propose aGeographic-Enhanced and Dependency-guIded sequence Tagging (GEDIT) model toconcurrently address the two challenges. First, to alleviate challenge #1, wedevelop a geographic-enhanced pre-trained model to learn the textrepresentations. Second, to mitigate challenge #2, we apply a relational graphconvolutional network to learn the tree node representations from the parseddependency tree. Finally, we construct a neural sequence tagging model byintegrating and feeding the previously pre-learned representations into a CRFlayer. Extensive experiments conducted on a real-world dataset demonstrate thesuperiority and effectiveness of GEDIT. In addition, it has already beendeployed in production at Baidu Maps. Statistics show that the proposedsolution can save significant human effort and labor costs to deal with thesame amount of documents, which confirms that it is a practical way for POIaccessibility maintenance.

GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Maps