Gao Cong

TL;DR: A novel approach to identifying erroneous sentences is proposed, which first mine labeled tree patterns and sequential patterns to characterize both erroneous and correct sentences, and which are utilized in two ways to distinguish correct sentences from erroneous sentences.

TL;DR: Different from existing recommender systems and data analysis systems, PreMiner highly personalizes user experience on maps and supports several applications, including user mobility & interests mining, opinion mining in regions, user recommendation, point-of-interest recommendation, and querying and subscribing on geo-textual data.

TL;DR: This work proposes to model the mapping problem as a ranking problem, and develop a method to learn a ranking function by exploiting the textual, visual and user information of photos, and proposes three subobjectives for learning the parameters of the proposed ranking function.

TL;DR: A corpus statistics association measure is employed to quantify the pairwise word dependencies and a generalized association-based unified framework to identify features, including explicit and implicit features, and opinion words from reviews is proposed.

TL;DR: In this article , the authors explore the potential of many existing pre-trained models by testing their performances on seven tasks across multiple geospatial subdomains including Geospatial Semantics, Health Geography, Urban Geography and Remote Sensing.