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- research-articleFebruary 2023
Location extraction from Traffic Event-related Text
IC3INA '22: Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its ApplicationsPages 331–335https://doi.org/10.1145/3575882.3575946One of the contents that will play an important role in the trip planning system is content related to geospatial. To ensure that the trip planning system is optimal and safe, information about traffic conditions, events, as well as their location is ...
- short-paperNovember 2022
Addressing the location A/B problem on Twitter: the next generation location inference research
LocalRec '22: Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and GeoadvertisingArticle No.: 4, Pages 1–4https://doi.org/10.1145/3557992.3565989Often, global and regional topics on Twitter across multiple thematic areas, such as disasters, politics, protests, entertainment, epidemics, literature, travel, culture, weather, etc., witness an unprecedented level of exchange of conversations. An ...
- research-articleApril 2018
SAVITR: A System for Real-time Location Extraction from Microblogs during Emergencies
WWW '18: Companion Proceedings of the The Web Conference 2018Pages 1643–1649https://doi.org/10.1145/3184558.3191623We present SAVITR, a system that leverages the information posted on the Twitter microblogging site to monitor and analyse emergency situations. Given that only a very small percentage of microblogs are geo-tagged, it is essential for such a system to ...
- research-articleJuly 2014
Fine-grained location extraction from tweets with temporal awareness
SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrievalPages 43–52https://doi.org/10.1145/2600428.2609582Twitter is a popular platform for sharing activities, plans, and opinions. Through tweets, users often reveal their location information and short term visiting plans. In this paper, we are interested in extracting fine-grained locations mentioned in ...
- research-articleMay 2013
Location extraction from disaster-related microblogs
WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide WebPages 1017–1020https://doi.org/10.1145/2487788.2488108Location information is critical to understanding the impact of a disaster, including where the damage is, where people need assistance and where help is available. We investigate the feasibility of applying Named Entity Recognizers to extract locations ...
- ArticleDecember 2012
Location Extraction from Social Networks with Commodity Software and Online Data
ICDMW '12: Proceedings of the 2012 IEEE 12th International Conference on Data Mining WorkshopsPages 827–834https://doi.org/10.1109/ICDMW.2012.128Location is prevalent in most applications nowadays, and is considered a first class citizen in social networks. Locational information is of great significance since it can be used to map information from the online back to the physical world, to ...
- demonstrationOctober 2009
TravelScope: standing on the shoulders of dedicated travelers
MM '09: Proceedings of the 17th ACM international conference on MultimediaPages 1021–1022https://doi.org/10.1145/1631272.1631499In this paper, we propose a system called TravelScope that helps users experience virtual tours by presenting information mined from user-generated travelogues and photos. The system can (1) recommend popular places for a given region; (2) characterize ...
- research-articleOctober 2009
Mining city landmarks from blogs by graph modeling
MM '09: Proceedings of the 17th ACM international conference on MultimediaPages 105–114https://doi.org/10.1145/1631272.1631289Recent years have witnessed great prosperity in community-contributed multimedia. Discovering, extracting, and summarizing knowledge from these data enables us to make better sense of the world. In this paper, we report our work on mining famous city ...
- ArticleSeptember 2009
Extracting Geospatial Entities from Wikipedia
ICSC '09: Proceedings of the 2009 IEEE International Conference on Semantic ComputingPages 450–457https://doi.org/10.1109/ICSC.2009.62This paper addresses the challenge of extracting geospatial data from the article text of the English Wikipedia. In the first phase of our work, we create a training corpus and select a set of word-based features to train a Support Vector Machine (SVM) ...