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

TL;DR

This paper introduces GEDIT, a novel sequence tagging model that combines geographic-enhanced pre-training and dependency-guided graph convolution to accurately extract POI and accessibility information from unstructured text, improving efficiency in dynamic POI databases.

Contribution

The paper presents GEDIT, a new model integrating geographic and dependency information for joint POI and accessibility extraction, addressing challenges of new POI names and multiple pairs in text.

Findings

GEDIT outperforms baseline models in accuracy on real-world datasets.

Deployment in Baidu Maps demonstrates practical efficiency gains.

Model reduces human effort and labor costs significantly.

Abstract

Providing timely accessibility reminders of a point-of-interest (POI) plays a vital role in improving user satisfaction of finding places and making visiting decisions. However, it is difficult to keep the POI database in sync with the real-world counterparts due to the dynamic nature of business changes. To alleviate this problem, we formulate and present a practical solution that jointly extracts POI mentions and identifies their coupled accessibility labels from unstructured text. We approach this task as a sequence tagging problem, where the goal is to produce <POI name, accessibility label> pairs from unstructured text. This task is challenging because of two main issues: (1) POI names are often newly-coined words so as to successfully register new entities or brands and (2) there may exist multiple pairs in the text, which necessitates dealing with one-to-many or many-to-one mapping to make each POI coupled with its accessibility label. To this end, we propose a Geographic-Enhanced and Dependency-guIded sequence Tagging (GEDIT) model to concurrently address the two challenges. First, to alleviate challenge #1, we develop a geographic-enhanced pre-trained model to learn the text representations. Second, to mitigate challenge #2, we apply a relational graph convolutional network to learn the tree node representations from the parsed dependency tree. Finally, we construct a neural sequence tagging model by integrating and feeding the previously pre-learned representations into a CRF layer. Extensive experiments conducted on a real-world dataset demonstrate the superiority and effectiveness of GEDIT. In addition, it has already been deployed in production at Baidu Maps. Statistics show that the proposed solution can save significant human effort and labor costs to deal with the same amount of documents, which confirms that it is a practical way for POI accessibility maintenance.